arXiv subjects

Jianhua Liu

Publications and source records attributed to Jianhua Liu.

5 recordsLinked to original sources

arXiv2026

A unified quantum electrical platform for synchronous metrological realization of volt, ohm and ampere

A co-located integration quantum electrical standard is essential to reduce reliance on distributed traceability in high-accuracy metrology, especially for portable and on-site use. Metrologically, realizing any two of voltage, resistance, and current is sufficient, as the third follows from Ohm's law. The combination of Josephson voltage and quantum Hall resistance offers better uncertainty, but conflicts with the tesla-level field for quantum Hall and near-zero field for Josephson operation. Here we report a compact unified platform enabling co-realization of quantum voltage and resistance in a single cryostat near 4 K, with quantum current derived via Ohm's law. A hierarchical magnetic shielding with staged attenuation and spatial confinement allows 6 T and below 50 nT to coexist within 270 mm axial separation with negligible cross-coupling. In integrated operation, the Josephson and quantum Hall subsystems agree with expected quantized values within relative standard uncertainties of 2.6E-9 and 1.4E-8, respectively. Linking them via an improved cryogenic current comparator realizes a 50 μA quantum current with relative uncertainty of 6.6E-8. These results demonstrate that three basic electrical units can be synchronously realized with superior metrological consistency on a single integrated platform, offering a viable transition from distributed calibration chains toward compact-integrated quantum-based realization.

physics.ins-det↗

arXiv2026

Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction

Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deployed on constrained edge devices. Flexible material-based neuromotor interfaces hold considerable promise, as they decode human movement intention into natural control. Although emerging flexible electronic skins enable wearable high-fidelity data acquisition, practical deployment inevitably involves trade-offs between computational resources and portability. We present a data-centric paradigm where physiological features yield fundamental separability, providing sufficient discriminative cues for recognition. A wireless, high-bandwidth system developed for collecting various electrophysiological signals, when integrated with muscle-specific electrodes, forms a surface electromyography-based interface. Exploiting highly separable data, a 2,210-parameter model achieves 94.36% accuracy across 34 gestures and can be rapidly deployed on edge devices, establishing a new thousand-parameter benchmark for dexterous decoding. The underlying data-algorithm interactions in the data-centric paradigm are further clarified, demonstrating its feasibility in real-world scenarios. This study provides a principled and validated pathway for practical deployment of reliable neuromotor interfaces.

cs.RO↗

arXiv2025

Beyond openness: Inclusiveness and usability of Chinese scholarly data in OpenAlex

OpenAlex, launched in 2022 as a fully open scholarly data source, promises greater inclusiveness compared to traditional proprietary databases. This study evaluates whether OpenAlex delivers on that promise by examining its coverage and metadata quality for Chinese-language journals and their articles. Using the 2023 edition of A Guide to the Core Journals of China (GCJC) and Wanfang Data as a benchmark, we analyze three aspects: (1) journal-level coverage, (2) article-level coverage, and (3) completeness and accuracy of metadata fields. Results show that OpenAlex indexes only 37% of GCJC journals and 24% of their articles, with substantial disciplinary and temporal variation. Metadata quality is uneven: while basic fields such as title and publication year are complete, bibliographic details, author affiliations, and cited references are frequently missing or inaccurate. DOI coverage is limited, and language information is often incorrect, with most Chinese-language articles labeled as English. These findings highlight significant challenges for achieving full inclusiveness and usability in research evaluation and related activities. We conclude with recommendations for improving data aggregation strategies, DOI registration practices, and metadata standardization to enhance the integration of local scholarly outputs into global open infrastructures.

cs.DL↗

arXiv2023

MEC: An Open-source Fine-grained Mapping Equivalence Checking Tool for FPGA

Technology mapping is an essential step in EDA flow. However, the function of the circuit may be changed after technology mapping, and equivalence checking (EC) based verification is highly necessary. The traditional EC method has significant time and resource constraints, making it only feasible to carry out at a coarse-grained level. To make it efficient for technology mapping, we propose a fine-grained method called MEC, which leverages a combination of two approaches to significantly reduce the time cost of verification. The local block verification approach performs fast verification and the global graph cover approach guarantees correctness. The proposed method is rigorously tested and compared to three EC tools, and the results show that MEC technique offers a substantial improvement in speed. MEC not only offers a faster and more efficient way of performing EC on technology mapping but also opens up new opportunities for more fine-grained verification in the future.

cs.AR↗

arXiv2020

Long-Range Gesture Recognition Using Millimeter Wave Radar

Millimeter wave (mmWave) based gesture recognition technology provides a good human computer interaction (HCI) experience. Prior works focus on the close-range gesture recognition, but fall short in range extension, i.e., they are unable to recognize gestures more than one meter away from considerable noise motions. In this paper, we design a long-range gesture recognition model which utilizes a novel data processing method and a customized artificial Convolutional Neural Network (CNN). Firstly, we break down gestures into multiple reflection points and extract their spatial-temporal features which depict gesture details. Secondly, we design a CNN to learn changing patterns of extracted features respectively and output the recognition result. We thoroughly evaluate our proposed system by implementing on a commodity mmWave radar. Besides, we also provide more extensive assessments to demonstrate that the proposed system is practical in several real-world scenarios.

cs.HC↗