arXiv subjects

Qingqing Chen

Publications and source records attributed to Qingqing Chen.

11 recordsLinked to original sources

arXiv2026

LoRIS: LoRaWAN-based IoT Platform for Sustainability Monitoring in Hotels

The hospitality sector is a major source of global greenhouse gas emissions, water stress, and waste generation, yet sustainability reporting in hotels remains constrained by coarse, manually collected operational data. We present LoRIS (LoRaWAN-based IoT platform for sustainability monitoring in hotels), a LoRaWAN-based sensing system that delivers high-resolution measurements of resource consumption, environmental conditions, and guest behaviour across geographically distributed hotel properties. The architecture follows the canonical LoRaWAN reference model and is built for the operational realities of hospitality deployments: restrictive hotel IT policies, guest privacy expectations, rapid and reversible installation, and multi-year battery operation. Privacy-by-design guides modality selection and deployment zoning, and end-to-end encryption protects data from sensor to dashboard. This system has been running since February 2022 and currently spans 850 sensors of 19 types across 21 sites in Australia and Slovenia, covering both the AU915 and EU868 regulatory regions. The platform has generated over 202 million sensor records and ingests approximately 245,000 uplink messages per day on managed serverless infrastructure. Our system has been successfully used for seven field studies spanning food waste, energy consumption, and water consumption, including controlled intervention experiments that measure environmental outcomes and guest satisfaction in parallel. This system shows that LoRaWAN sensing can be deployed at scale in operational hotels without compromising guest experience or privacy.

cs.NI↗

arXiv2025

Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data's non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel's features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance.

cs.LG↗

arXiv2024

Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging

In clinical practice, segmenting specific lesions based on the needs of physicians can significantly enhance diagnostic accuracy and treatment efficiency. However, conventional lesion segmentation models lack the flexibility to distinguish lesions according to specific requirements. Given the practical advantages of using text as guidance, we propose a novel model, Language-guided Scale-aware MedSegmentor (LSMS), which segments target lesions in medical images based on given textual expressions. We define this as a new task termed Referring Lesion Segmentation (RLS). To address the lack of suitable benchmarks for RLS, we construct a vision-language medical dataset named Reference Hepatic Lesion Segmentation (RefHL-Seg). LSMS incorporates two key designs: (i) Scale-Aware Vision-Language attention module, which performs visual feature extraction and vision-language alignment in parallel. By leveraging diverse convolutional kernels, this module acquires rich visual representations and interacts closely with linguistic features, thereby enhancing the model's capacity for precise object localization. (ii) Full-Scale Decoder, which globally models multi-modal features across multiple scales and captures complementary information between them to accurately delineate lesion boundaries. Additionally, we design a specialized loss function comprising both segmentation loss and vision-language contrastive loss to better optimize cross-modal learning. We validate the performance of LSMS on RLS as well as on conventional lesion segmentation tasks across multiple datasets. Our LSMS consistently achieves superior performance with significantly lower computational cost. Code and datasets will be released.

cs.CV↗

arXiv2024

Stable Optimization for Large Vision Model Based Deep Image Prior in Cone-Beam CT Reconstruction

Large Vision Model (LVM) has recently demonstrated great potential for medical imaging tasks, potentially enabling image enhancement for sparse-view Cone-Beam Computed Tomography (CBCT), despite requiring a substantial amount of data for training. Meanwhile, Deep Image Prior (DIP) effectively guides an untrained neural network to generate high-quality CBCT images without any training data. However, the original DIP method relies on a well-defined forward model and a large-capacity backbone network, which is notoriously difficult to converge. In this paper, we propose a stable optimization method for the forward-model-free, LVM-based DIP model for sparse-view CBCT. Our approach consists of two main characteristics: (1) multi-scale perceptual loss (MSPL) which measures the similarity of perceptual features between the reference and output images at multiple resolutions without the need for any forward model, and (2) a reweighting mechanism that stabilizes the iteration trajectory of MSPL. One shot optimization is used to simultaneously and stably reweight MSPL and optimize LVM. We evaluate our approach on two publicly available datasets: SPARE and Walnut. The results show significant improvements in both image quality metrics and visualization that demonstrates reduced streak artifacts. The source code is available upon request.

eess.IV↗

arXiv2023

A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification

There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In addition, due to inter-individual differences in EEG signals, this discrepancy results in new subjects need spend a amount of calibration time for EEG-based motor imagery brain-computer interface. In order to solve the above problems, we propose a Dynamic Domain Adaptation Based Deep Learning Network (DADL-Net). First, the EEG data is mapped to the three-dimensional geometric space and its temporal-spatial features are learned through the 3D convolution module, and then the spatial-channel attention mechanism is used to strengthen the features, and the final convolution module can further learn the spatial-temporal information of the features. Finally, to account for inter-subject and cross-sessions differences, we employ a dynamic domain-adaptive strategy, the distance between features is reduced by introducing a Maximum Mean Discrepancy loss function, and the classification layer is fine-tuned by using part of the target domain data. We verify the performance of the proposed method on BCI competition IV 2a and OpenBMI datasets. Under the intra-subject experiment, the accuracy rates of 70.42% and 73.91% were achieved on the OpenBMI and BCIC IV 2a datasets.

eess.SP↗

arXiv2023

Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance

High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3D images remains a challenge due to their high spatial resolution and dimensionality in contrast to currently limited GPU memory. Therefore, most existing 3D image segmentation methods use patch-based models, which have low inference efficiency and ignore global contextual information. To address these problems, we propose a super-resolution (SR) based patch-free 3D image segmentation framework that can realize HR segmentation from a global-wise low-resolution (LR) input. The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input. To furthermore balance the information loss with the LR input, we propose a High-Frequency Guidance Module (HGM), and design an efficient selective cropping algorithm to crop an HR patch from the original image as restoration guidance for it. In addition, we also propose a Task-Fusion Module (TFM) to exploit the inter connections between segmentation and SR task, realizing joint optimization of the two tasks. When predicting, only the main segmentation task is needed, while other modules can be removed for acceleration. The experimental results on two different datasets show that our framework has a four times higher inference speed compared to traditional patch-based methods, while its performance also surpasses other patch-based and patch-free models.

eess.IV↗

arXiv2023

Electronic properties of 2{\it H}-stacking bilayer MoS$_2$ measured by terahertz time-domain spectroscopy

Bilayer (BL) molybdenum disulfide (MoS$_2$) is one of the most important electronic structures not only in valleytronics but also in realizing twistronic systems on the basis of the topological mosaics in Moiré superlattices. In this work, BL MoS$_2$ on sapphire substrate with 2$H$-stacking structure is fabricated. We apply the terahertz (THz) time-domain spectroscopy (TDS) for examining the basic optoelectronic properties of this kind of BL MoS$_2$. The optical conductivity of BL MoS$_2$ is obtained in temperature regime from 80 to 280 K. Through fitting the experimental data with the theoretical formula, the key sample parameters of BL MoS$_2$ can be determined, such as the electron density, the electronic relaxation time and the electronic localization factor. The temperature dependence of these parameters is examined and analyzed. We find that, similar to monolayer (ML) MoS$_2$, BL MoS$_2$ with 2$H$-stacking can respond strongly to THz radiation field and show semiconductor-like optoelectronic features. The theoretical calculations using density functional theory (DFT) can help us to further understand why the THz optoelectronic properties of BL MoS$_2$ differ from those observed for ML MoS$_2$. The results obtained from this study indicate that the THz TDS can be applied suitably to study the optoelectronic properties of BL MoS$_2$ based twistronic systems for novel applications as optical and optoelectronic materials and devices.

cond-mat.mtrl-sci↗

arXiv2021

Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or channel-attention based concatenation. However, these methods ignored the spatial (pixel-wise) relationships between different phases, hence leading to insufficient feature integration. In addition, the performance of existing methods remains subject to the uncertainty in segmentation, which is particularly acute in tumor boundary regions. In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation. To this end, we introduce a spatial aggregation module (SAM), which encourages per-pixel interactions between different phases, to make full use of cross-phase information. Moreover, we devise an uncertain region inpainting module (URIM) to refine uncertain pixels using neighboring discriminative features. Experiments on an in-house multi-phase CT dataset of focal liver lesions (MPCT-FLLs) demonstrate that our method achieves promising liver tumor segmentation and outperforms state-of-the-arts.

eess.IV↗

arXiv2021

PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images

In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intra-phase attention (Intra-PA) module and an inter-phase attention (Inter-PA) module to capture channel-wise self-dependencies and cross-phase interdependencies, respectively. Thus it enables the network to learn more representative multi-phase features by refining the PV features according to the channel dependencies and recalibrating the ART features based on the learned interdependencies between phases. We propose a PA-based multi-scale fusion (MSF) architecture to embed the PA blocks in the network at multiple levels along the encoding path to fuse multi-scale features from multi-phase images. Moreover, a 3D boundary-enhanced loss (BE-loss) is proposed for training to make the network more sensitive to boundaries. To evaluate the performance of our proposed PA-ResSeg, we conducted experiments on a multi-phase CT dataset of focal liver lesions (MPCT-FLLs). Experimental results show the effectiveness of the proposed method by achieving a dice per case (DPC) of 0.77.87, a dice global (DG) of 0.8682, a volumetric overlap error (VOE) of 0.3328 and a relative volume difference (RVD) of 0.0443 on the MPCT-FLLs. Furthermore, to validate the effectiveness and robustness of PA-ResSeg, we conducted extra experiments on another multi-phase liver tumor dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637 and a RVD of 0.0163. The proposed method shows its robustness and generalization capability in different datasets and different backbones.

eess.IV↗

arXiv2018

Interference effect in the optomechanical stochastic resonance

In this paper, we study the stochastic resonance (SR) effect in an optomechanical system driven by a strong coupling field and two weak signals in both semiclassical and quantum frameworks. In the semiclassical description, the SR phenomena are found at the cooperation of input signals and system noises. When two signals co-act on our system, the interference effect between the optically induced SR and the mechanically induced SR can be generated. In particular, a unique beating effect, which makes the SR effect robust against the initial phase difference of two signals, appears in the SR synchronization process with unsynchronized signals. In addition, the quantum stochastic resonance effect is numerically observed in the full quantum framework induced by pure quantum fluctuations.

physics.optics↗

arXiv2014

Emulated ASIC Power and Temperature Monitor System for FPGA Prototyping of an Invasive MPSoC Computing Architecture

In this contribution the emulation of an ASIC temperature and power monitoring system (TPMon) for FPGA prototyping is presented and tested to control processor temperatures under different control targets and operating strategies. The approach for emulating the power monitor is based on an instruction-level energy model. For emulating the temperature monitor, a thermal RC model is used. The monitoring system supplies an invasive MPSoC computing architecture with hardware status information (power and temperature data of the processors within the system). These data are required for resource-aware load distribution. As a proof of concept different operating strategies and control targets were evaluated for a 2-tile invasive MPSoC computing system.

cs.AR↗