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Autonomous embodied navigation task generation from natural language dialogues

Science China(Information Sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration technology

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Chinese Journal of electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Real-time Adaptation of Deep Learning Walking Speed Estimators Enables Biomimetic Assistance Modulation in an Open-Source Bionic Leg

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 711-722页

作者： Maldonado-Contreras, Jairo Johnson, Cole Zhou, Sixu Kim, Hanjun Knight, Ian Herrin, Kinsey R. Young, Aaron J. Georgia Institute of Technology Mechanical Engineering Department AtlantaGA30332 United States Georgia Institute of Technology Institute of Robotics and Intelligent Machines AtlantaGA30332 United States Georgia Institute of Technology School of Computer Science AtlantaGA30332 United States

This study introduces a novel continual learning algorithm that incrementally improves the performance of deep-learning-based walking speed estimators during level-ground walking with a powered knee-ankle prosthesis. While user-dependent (DEP) estimators generally outperform user-independent (IND) estimators, they require the pre-collection of DEP training data. In contrast, our real-time algorithm adapts IND estimators to self-labeled DEP data generated during walking, eliminating the need for pre-collected datasets. The algorithm also features a biomimetic scaling mechanism that adjusts prosthetic assistance based on speed estimates. We evaluated our algorithm on novel subjects (N=10) with unilateral above-knee amputations during treadmill and overground walking. For treadmill trials, when adapted with estimated and ground truth labels, estimators achieved mean absolute errors (MAEs) of 0.074 0.023 (mean, standard deviation) and 0.074 0.018 m/s, respectively, reflecting a significant 28% (p © 2018 IEEE.

关键词： Knee prostheses

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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Science China(Information Sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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Architecture Decisions for Supporting Web-based Visualization of Distribution System Operating Envelopes

Architecture Decisions for Supporting Web-based Visualizatio...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Fernandez, Jorge Grijalva, Santiago School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331541125

Operating envelopes are dynamic limits on power injections, computed in advance based on the operational con-ditions of the network, such as load profiles. These dynamic limits can unlock the full potential of distributed energy resources (DERs) by maximizing the use of existing infrastructure while ensuring a reliable power supply. Visualizing operating envelopes presents a significant challenge, it requires effectively conveying spatio-temporal information for multiple connection sites simultaneously. This paper discusses, demonstrates, and evaluates a set of software architecture decisions to support web-based visualization of distribution system operating envelopes. We propose a microservice-oriented architecture with two processing modules: one for time series power flow and another for three-phase unbalanced multi-period optimal power flow (also known as energy scheduling) computations. Both modules are con-tainerized, ensuring strong separation of duties and portability. Additionally, a web-based visualization interface is developed to incorporate advanced interactive visualization techniques to support the analysis of complex spatio-temporal relationships and 'what if' scenarios. The performance of the approach is evaluated by measuring processing time and size from responses considering various distribution feeders, including the 3- Bus, IEEE 4-Bus, IEEE 13-Bus, and IEEE 123-Bus test feeders. © 2025 IEEE.

关键词： Electric load flow

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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food Science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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Towards Harsh Environment Silicon Carbide Based On-chip Antenna

Towards Harsh Environment Silicon Carbide Based On-chip Ante...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Dasari, Sree Adinarayana Lee, Seung Yoon Ghalichechian, Nima School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781946815200

This paper presents an on-chip dielectric resonator antenna (DRA) based on silicon carbide (SiC). SiC is a semiconductor material known for its large bandgap, high dielectric breakdown, and temperature-stable dielectric properties, making it well-suited for harsh environment electronics. We propose a DRA on a SiC substrate, incorporating a sapphire rectangular resonator bonded to the SiC chip. The fundamental TE111 mode is excited at 29.5 GHz within the resonator via a rectangular aperture on the SiC chip, which is fed by a coplanar waveguide (CPW). An inductive series stub at the feed is used for impedance matching. Simulation results demonstrate a high gain of 7.4 dBi with a radiation efficiency of 84%. Furthermore, we propose a custom probe station with an integrated hot plate and robotic arm for high-temperature testing of the DRA. © 2025 USNC-URSI.

关键词： Sapphire

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A Novel Contingency Analysis-Driven Deep Learning Framework for Evaluation of a Primary Power System Security Metric

A Novel Contingency Analysis-Driven Deep Learning Framework ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： King, Adam Grijalva, Santiago School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798331541125

Continuous operation of power systems is critical to modern society, even during unexpected or emergency situations. One method to evaluate the security of power systems is contingency analysis, a computationally-intensive process which considers how a set of possible scenarios would affect the electrical grid. This paper presents a novel security evaluation method which enables a topology- and parameter-free estimation of a system-level security metric, the system aggregate megawatt contingency overload (SysAMWCO), by leveraging a novel deep learning-based approach. Using the results of conventional contingency analysis for a simulated power system, a deep neural network (DNN) is trained to predict the SysAMWCO value using only bus loads as input. Experimentation across different neural network parameters reveals a complex relationship between these parameters and the accuracy of the predicted SysAMWCO value. While each DNN is trained on data generated using conventional contingency analysis methods, fully-trained models achieve R-squared scores in excess of 0.98 without needing electrical grid topology or power device parameter information as input. Given these results, this framework shows promise for potential real-world applications. © 2025 IEEE.

关键词： Power system analysis

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Fully Digital Ultra-low-bit Linear Embedding Beamforming System with Quantization Compensation

Fully Digital Ultra-low-bit Linear Embedding Beamforming Sys...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Mao, Xiangyu Delude, Coleman Wang, Wei-Chun Romberg, Justin Mukhopadhyay, Saibal Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350377743

We propose the first digital ultra-low-bit precision implementation of a Linear Embedded Beamforming (LE-BF) system, which transforms high-dimensional input signals into low-dimensional outputs. The use of quantization compensation in this operation ensures beamforming performance is preserved. The system then processes the low-dimensional outputs to generate the final output with lower power consumption. The paper presents two different implementations of LE-BF systems: 1) An FPGA-based 2-bit LE-BF system configured with 64 inputs and 1 output, achieving a 12.4× decrease in design component utilization and a 11.2× decrease in power consumption compared to corresponding traditional True-Time-Delay digital beamforming (TTD-DBF) designs. 2) An ASIC-based (TSMC 28nm) 2-bit LE-BF configured with 256 inputs and 1 output, exhibiting a 18.7× greater efficiency in both design area and power consumption compared to corresponding TTDDBF designs. The ASIC-based system is capable of supporting a 1 GHz instantaneous bandwidth. © 2025 IEEE.

关键词： Beamforming

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