Reed membrane,a natural cellulosic material traditionally used in musical instruments,holds promise in flexible electronics due to its abundance,low cost,and excellent biocompatibility.However,its native form contains...Reed membrane,a natural cellulosic material traditionally used in musical instruments,holds promise in flexible electronics due to its abundance,low cost,and excellent biocompatibility.However,its native form contains water-soluble ions and lipid-soluble waxes that hinder performance in acoustic and electronics by compromising electrical insulation and mechanical stability.Here,supercritical fluid superposition purification(SCSP-WA)is introduced,which utilizes supercritical CO_(2)with water and acetone as bipolar co-solvents to selectively remove these impurities.Post-SCSP-WA treatment,the reed membrane exhibits significant enhancements in mechanical strength and electrical insulation,achieving a 4-fold increase in elongation at break,improved tensile strength and Young’s modulus,and a 98.5%reduction in leakage current,all while maintaining low and stable capacitance.These improvements stem from the restructuring of the fibrous network into a porous,interconnected microstructure.Material characterization(X-ray photoelectron spectroscopy(XPS),Fourier-transform infrared spectroscopy(FTIR),and scanning electron microscopy(SEM))confirmed the effective removal of magnesium and waxy functional groups,along with enhanced fiber crosslinking.Cytotoxicity tests further validated the biocompatibility of the SCSP-WA-treated membranes.This environmentally sustainable approach expands the potential of reed membranes in flexible bioelectronics and bio-integrated acoustic systems.展开更多
In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In additi...In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.展开更多
Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.S...Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.展开更多
This year marks the tenth anniversary of the State Key Laboratory of Advanced Displays and Optoelectronics Technologies(SKLADOT)at the Hong Kong University of Science and Technology(HKUST).The predecessor of SKLADOT w...This year marks the tenth anniversary of the State Key Laboratory of Advanced Displays and Optoelectronics Technologies(SKLADOT)at the Hong Kong University of Science and Technology(HKUST).The predecessor of SKLADOT was the Center for Display Research(CDR)which was started in 1995.Thus display research has a long history at HKUST.展开更多
Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces ...Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.展开更多
Artificial intelligence(AI)is pivotal in advancing fifth-generation(5G)-Advanced and sixthgeneration systems,capturing substantial research interest.Both the 3rd Generation Partnership Project(3GPP)and leading corpora...Artificial intelligence(AI)is pivotal in advancing fifth-generation(5G)-Advanced and sixthgeneration systems,capturing substantial research interest.Both the 3rd Generation Partnership Project(3GPP)and leading corporations champion AI’s standardization in wireless communication.This piece delves into AI’s role in channel state information(CSI)prediction,a sub-use case acknowledged in 5GAdvanced by the 3GPP.We offer an exhaustive survey of AI-driven CSI prediction,highlighting crucial elements like accuracy,generalization,and complexity.Further,we touch on the practical side of model management,encompassing training,monitoring,and data gathering.Moreover,we explore prospects for CSI prediction in future wireless communication systems,entailing integrated design with feedback,multitasking synergy,and predictions in rapid scenarios.This article seeks to be a touchstone for subsequent research in this burgeoning domain.展开更多
Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap fr...Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.展开更多
Besides the common short-channel effect(SCE)of threshold voltage(V_(th))roll-off during the channel length(L)downscaling of In GaZnO(IGZO)thin-film transistors(TFTs),an opposite V_(th)roll-up was reported in this work...Besides the common short-channel effect(SCE)of threshold voltage(V_(th))roll-off during the channel length(L)downscaling of In GaZnO(IGZO)thin-film transistors(TFTs),an opposite V_(th)roll-up was reported in this work.Both roll-off and roll-up effects of Vth were comparatively investigated on IGZO transistors with varied gate insulator(GI),source/drain(S/D),and device architecture.For IGZO transistors with thinner GI,the SCE was attenuated due to the enhanced gate controllability over the variation of channel carrier concentration,while the Vth roll-up became more noteworthy.The latter was found to depend on the relative ratio of S/D series resistance(R_(SD))over channel resistance(R_(CH)),as verified on transistors with different S/D.Thus,an ideal S/D engineering with small R_(SD)but weak dopant diffusion is highly expected during the downscaling of L and GI in IGZO transistors.展开更多
Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing ...Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing and quadruplexing Substrate-Integrated Waveguide (SIW) cavities. The diplexing structure incorporates two V-shaped slots, while the quadruplexing structure advances this concept by combining the slots to form a cross-shaped configuration within the cavity. The widths and lengths of the slots are carefully tuned to achieve variations in the respective operating frequencies without affecting the others. The proposed diplexing antenna resonates at 8.48 and 9.2 GHz, with a frequency ratio of 1.08, while the quadruplexing antenna operates at 6.9, 7.1, 7.48, and 8.2GHz. Both designs exhibit isolation levels well below –20dB and achieve a simulated peak gain of 5.6 dBi at the highest frequency, with a compact cavity area of 0.56 λg^(2). The proposed antennas operate within the NR bands (n12, n18, n26), making them suitable for modern high-speed wireless communication systems. Moreover, the properties like multiband operation, compactness, high isolation, low loss, and low interference make the antenna favorable for the high-speed railway communication systems.展开更多
Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are use...Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are used in reality.It is almost impossible to use a CW signal to predict system performances,such as error vector magnitude(EVM),bit error rate(BER),etc.,of a transceiver front-end when dealing with complex modulated signals.This paper develops an integrated system evaluation engine(ISEE)to evaluate the system performances of a transceiver front-end or its sub-circuits.This crossdomain simulation platform is based on Matlab,advanced design system(ADS),and Cadence simulators to link the baseband signals and transceiver frond-end.An orthogonal frequency division multiplex(OFDM)modem is implemented in Matlab for evaluating the system performances.The modulated baseband signal from Matlab is dynamically fed into ADS,which includes transceiver front-end for co-simulation.The sub-block circuits of the transceiver front-end can be implemented using ADS and Cadence simulators.After system-level circuit simulation in ADS,the output signal is dynamically delivered to Matlab for demodulation.To simplify the use of the co-simulation platform,a graphical user interface(GUI)is constructed using Matlab.The parameters of the OFDM signals can be easily reconfigured on the GUI to simulate RF circuits with different modulation schemes.To demonstrate the effectiveness of the ISEE,a 3.5 GHz power amplifier is simulated and characterized using 20 MHz 16-and 64-QAM OFDM signals.展开更多
Human skin exhibits a remarkable capability to perceive contact forces and environmental temperatures,providing complex information that is essential for its subtle control.Despite recent advancements in soft tactile ...Human skin exhibits a remarkable capability to perceive contact forces and environmental temperatures,providing complex information that is essential for its subtle control.Despite recent advancements in soft tactile sensors,accurately decoupling signals—specifically separating forces from directional orientation and temperature—remains a challenge thus resulting in failure to meet the advanced application requirements of robots.This study proposes,F3T,a multilayer soft sensor unit designed to achieve isolated measurements and mathematical decoupling of normal pressure,omnidirectional tangential forces,and temperature.We developed a circular coaxial magnetic film featuring a floating mount multilayer capacitor that facilitated the physical decoupling of normal and tangential forces in all directions.Additionally,we incorporated an ion gel-based temperature-sensing film into the tactile sensor.The proposed sensor was resilient to external pressures and deformations,and could measure temperature and significantly eliminate capacitor errors induced by environmental temperature changes.In conclusion,our novel design allowed for the decoupled measurement of multiple signals,laying the foundation for advancements in high-level robotic motion control,autonomous decision-making,and task planning.展开更多
Thermophilic proteins maintain their structure and function at high temperatures,making them widely useful in industrial applications.Due to the complexity of experimental measurements,predicting the melting temperatu...Thermophilic proteins maintain their structure and function at high temperatures,making them widely useful in industrial applications.Due to the complexity of experimental measurements,predicting the melting temperature(T_(m))of proteins has become a research hotspot.Previous methods rely on amino acid composition,physicochemical properties of proteins,and the optimal growth temperature(OGT)of hosts for T_(m)prediction.However,their performance in predicting T_(m)values for thermophilic proteins(T_(m)>60℃)are generally unsatisfactory due to data scarcity.Herein,we introduce T_(m)Pred,a T_(m)prediction model for thermophilic proteins,that combines protein language model,graph convolutional network and Graphormer module.For performance evaluation,T_(m)Pred achieves a root mean square error(RMSE)of 5.48℃,a pearson correlation coefficient(P)of 0.784,and a coefficient of determination(R~2)of 0.613,representing improvements of 19%,15%,and 32%,respectively,compared to the state-of-the-art predictive models like DeepTM.Furthermore,T_(m)Pred demonstrated strong generalization capability on independent blind test datasets.Overall,T_(m)Pred provides an effective tool for the mining and modification of thermophilic proteins by leveraging deep learning.展开更多
Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven...Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.展开更多
Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs...Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced problems.To alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current study.In the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity.Moreover,the computational resources can be automatically allocated among all the subspaces.The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced MMOPs.The results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced MMOPs.Also,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically.展开更多
The terahertz band lies between the microwave and infrared regions of the electromagnetic spectrum.This radiation has very low photon energy and thus it does not pose any ionization hazard for biological tissues.It is...The terahertz band lies between the microwave and infrared regions of the electromagnetic spectrum.This radiation has very low photon energy and thus it does not pose any ionization hazard for biological tissues.It is strongly attenuated by water and very sensitive to water content.Unique absorption spectra due to intermolecular vibrations in this region have been found in different biological materials.These unique features make tera-hertz imaging very attractive for medical applications in order to provide complimentary information to existing imaging techniques.There has been an increasing interest in terahertz imaging and spectroscopy of biologically related applications within the last few years and more and more terahertz spectra are being reported.This paper introduces terahertz technology and provides a short review of recent advances in terahertz imaging and spectroscopy techniques,and a number of applications such as molecular spectroscopy,tissue characterization and skin imaging are discussed.展开更多
In recent years, there have been a significant number of demonstrations of small metallic and plasmonic lasers. The vast majority of these demonstrations have been for optically pumped devices. Electrically pumped dev...In recent years, there have been a significant number of demonstrations of small metallic and plasmonic lasers. The vast majority of these demonstrations have been for optically pumped devices. Electrically pumped devices are advantageous for applications and could demonstrate concepts not amenable for optical pumping. However, there have been relatively few demonstrations of electrically pumped small metal cavity lasers. This lack of results is due to the following reasons： there are limited types of electrically pumped gain media available; there is a significantly greater level of complexity required in the fabrication of electrically pumped devices; finally, the required components for electrical pumping restrict cavity design options and furthermore make it intrinsically more difficult to achieve lasing. This review looks at the motivation for electrically pumped nanolasers, the key issues that need addressing for them to be realized, the results that have been achieved so far including devices where lasing has not been achieved, and potential new directions that could be pursued.展开更多
Poly(methyl methacrylate)(PMMA) is widely used for graphene transfer and device fabrication.However,it inevitably leaves a thin layer of polymer residues after acetone rinsing and leads to dramatic degradation of devi...Poly(methyl methacrylate)(PMMA) is widely used for graphene transfer and device fabrication.However,it inevitably leaves a thin layer of polymer residues after acetone rinsing and leads to dramatic degradation of device performance.How to eliminate contamination and restore clean surfaces of graphene is still highly demanded.In this paper,we present a reliable and position-controllable method to remove the polymer residues on graphene films by laser exposure.Under proper laser conditions,PMMA residues can be substantially reduced without introducing defects to the underlying graphene.Furthermore,by applying this laser cleaning technique to the channel and contacts of graphene fieldeffect transistors(GFETs),higher carrier mobility as well as lower contact resistance can be realized.This work opens a way for probing intrinsic properties of contaminant-free graphene and fabricating high-performance GFETs with both clean channel and intimate graphene/metal contact.展开更多
An effective and low-cost front-side anti-reflection(AR) technique has long been sought to enhance the performance of highly efficient photovoltaic devices due to its capability of maximizing the light absorption in p...An effective and low-cost front-side anti-reflection(AR) technique has long been sought to enhance the performance of highly efficient photovoltaic devices due to its capability of maximizing the light absorption in photovoltaic devices. In order to achieve high throughput fabrication of nanostructured flexible and anti-reflection films, large-scale, nano-engineered wafer molds were fabricated in this work. Additionally, to gain in-depth understanding of the optical and electrical performance enhancement with AR films on polycrystalline Si solar cells, both theoretical and experimental studies were performed. Intriguingly,the nanocone structures demonstrated an efficient light trapping effect which reduced the surface reflection of a solar cell by17.7% and therefore enhanced the overall electric output power of photovoltaic devices by 6% at normal light incidence. Notably, the output power improvement is even more significant at a larger light incident angle which is practically meaningful for daily operation of solar panels. The application of the developed AR films is not only limited to crystalline Si solar cells explored here, but also compatible with any types of photovoltaic technology for performance enhancement.展开更多
The efficiency of any energy system can be charaterised by the relevant efficiency components in terms of performance, operation, equipment and technology(POET). The overall energy efficiency of the system can be opti...The efficiency of any energy system can be charaterised by the relevant efficiency components in terms of performance, operation, equipment and technology(POET). The overall energy efficiency of the system can be optimised by studying the POET energy efficiency components. For an existing energy system, the improvement of operation efficiency will usually be a quick win for energy efficiency. Therefore, operation efficiency improvement will be the main purpose of this paper. General procedures to establish operation efficiency optimisation models are presented. Model predictive control, a popular technique in modern control theory, is applied to solve the obtained energy models. From the case studies in water pumping systems, model predictive control will have a prosperous application in more energy efficiency problems.展开更多
基金supported by the Shenzhen Scientific and Technological Foundation(RCYX20231211090332037 and JCYJ20240813160211015)the National Natural Science Foundation of China(62474008 and 62204007)+2 种基金the Guangdong Provincial Natural Science Foundation(2024A1515030044)the Guangdong Provincial Key Laboratory of In-Memory Computing Chips(2024B1212020002)the Shenzhen Science and Technology Program(KJZD20230923115005009)。
文摘Reed membrane,a natural cellulosic material traditionally used in musical instruments,holds promise in flexible electronics due to its abundance,low cost,and excellent biocompatibility.However,its native form contains water-soluble ions and lipid-soluble waxes that hinder performance in acoustic and electronics by compromising electrical insulation and mechanical stability.Here,supercritical fluid superposition purification(SCSP-WA)is introduced,which utilizes supercritical CO_(2)with water and acetone as bipolar co-solvents to selectively remove these impurities.Post-SCSP-WA treatment,the reed membrane exhibits significant enhancements in mechanical strength and electrical insulation,achieving a 4-fold increase in elongation at break,improved tensile strength and Young’s modulus,and a 98.5%reduction in leakage current,all while maintaining low and stable capacitance.These improvements stem from the restructuring of the fibrous network into a porous,interconnected microstructure.Material characterization(X-ray photoelectron spectroscopy(XPS),Fourier-transform infrared spectroscopy(FTIR),and scanning electron microscopy(SEM))confirmed the effective removal of magnesium and waxy functional groups,along with enhanced fiber crosslinking.Cytotoxicity tests further validated the biocompatibility of the SCSP-WA-treated membranes.This environmentally sustainable approach expands the potential of reed membranes in flexible bioelectronics and bio-integrated acoustic systems.
基金supported by the 2024 Research Fund of University of Ulsan.
文摘In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.
文摘Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.
文摘This year marks the tenth anniversary of the State Key Laboratory of Advanced Displays and Optoelectronics Technologies(SKLADOT)at the Hong Kong University of Science and Technology(HKUST).The predecessor of SKLADOT was the Center for Display Research(CDR)which was started in 1995.Thus display research has a long history at HKUST.
基金supported by the Technology Innovation Program(20023566,‘Development and Demonstration of Industrial IoT and AI-Based Process Facility Intelligence Support System in Small and Medium Manufacturing Sites’)funded by the Ministry of Trade,Industry,&Energy(MOTIE,Republic of Korea).
文摘Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62261160576the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022-1 and BE2023022+3 种基金the Fundamental Research Funds for the Central Universities under Grant 2242023K5003The work was also supported in part by the NSFC under Grant 62401640in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515110732This work of Zhang Jun was supported partly by the Hong Kong Research Grants Council,Hong Kong,China under the NSFC/RGC Collaborative Research Scheme grant CRS HKUST603/22.
文摘Artificial intelligence(AI)is pivotal in advancing fifth-generation(5G)-Advanced and sixthgeneration systems,capturing substantial research interest.Both the 3rd Generation Partnership Project(3GPP)and leading corporations champion AI’s standardization in wireless communication.This piece delves into AI’s role in channel state information(CSI)prediction,a sub-use case acknowledged in 5GAdvanced by the 3GPP.We offer an exhaustive survey of AI-driven CSI prediction,highlighting crucial elements like accuracy,generalization,and complexity.Further,we touch on the practical side of model management,encompassing training,monitoring,and data gathering.Moreover,we explore prospects for CSI prediction in future wireless communication systems,entailing integrated design with feedback,multitasking synergy,and predictions in rapid scenarios.This article seeks to be a touchstone for subsequent research in this burgeoning domain.
基金supported in part by NSFC under Grant 62422407in part by RGC under Grant 26204424in part by ACCESS–AI Chip Center for Emerging Smart Systems, sponsored by the Inno HK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government
文摘Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.
基金supported financially by National key Research and Development Program under Grant 2021YFB3600802Shenzhen Municipal Scientific Program under Grant KJZD20230923114111021。
文摘Besides the common short-channel effect(SCE)of threshold voltage(V_(th))roll-off during the channel length(L)downscaling of In GaZnO(IGZO)thin-film transistors(TFTs),an opposite V_(th)roll-up was reported in this work.Both roll-off and roll-up effects of Vth were comparatively investigated on IGZO transistors with varied gate insulator(GI),source/drain(S/D),and device architecture.For IGZO transistors with thinner GI,the SCE was attenuated due to the enhanced gate controllability over the variation of channel carrier concentration,while the Vth roll-up became more noteworthy.The latter was found to depend on the relative ratio of S/D series resistance(R_(SD))over channel resistance(R_(CH)),as verified on transistors with different S/D.Thus,an ideal S/D engineering with small R_(SD)but weak dopant diffusion is highly expected during the downscaling of L and GI in IGZO transistors.
文摘Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing and quadruplexing Substrate-Integrated Waveguide (SIW) cavities. The diplexing structure incorporates two V-shaped slots, while the quadruplexing structure advances this concept by combining the slots to form a cross-shaped configuration within the cavity. The widths and lengths of the slots are carefully tuned to achieve variations in the respective operating frequencies without affecting the others. The proposed diplexing antenna resonates at 8.48 and 9.2 GHz, with a frequency ratio of 1.08, while the quadruplexing antenna operates at 6.9, 7.1, 7.48, and 8.2GHz. Both designs exhibit isolation levels well below –20dB and achieve a simulated peak gain of 5.6 dBi at the highest frequency, with a compact cavity area of 0.56 λg^(2). The proposed antennas operate within the NR bands (n12, n18, n26), making them suitable for modern high-speed wireless communication systems. Moreover, the properties like multiband operation, compactness, high isolation, low loss, and low interference make the antenna favorable for the high-speed railway communication systems.
基金supported by the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(No.HZQB-KCZYB-2020083).
文摘Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are used in reality.It is almost impossible to use a CW signal to predict system performances,such as error vector magnitude(EVM),bit error rate(BER),etc.,of a transceiver front-end when dealing with complex modulated signals.This paper develops an integrated system evaluation engine(ISEE)to evaluate the system performances of a transceiver front-end or its sub-circuits.This crossdomain simulation platform is based on Matlab,advanced design system(ADS),and Cadence simulators to link the baseband signals and transceiver frond-end.An orthogonal frequency division multiplex(OFDM)modem is implemented in Matlab for evaluating the system performances.The modulated baseband signal from Matlab is dynamically fed into ADS,which includes transceiver front-end for co-simulation.The sub-block circuits of the transceiver front-end can be implemented using ADS and Cadence simulators.After system-level circuit simulation in ADS,the output signal is dynamically delivered to Matlab for demodulation.To simplify the use of the co-simulation platform,a graphical user interface(GUI)is constructed using Matlab.The parameters of the OFDM signals can be easily reconfigured on the GUI to simulate RF circuits with different modulation schemes.To demonstrate the effectiveness of the ISEE,a 3.5 GHz power amplifier is simulated and characterized using 20 MHz 16-and 64-QAM OFDM signals.
基金support by Hong Kong RGC General Research Fund(16217824,16213825,16203923,and 16217824)National Natural Science Foundation of China(N_HKUST638/23)+1 种基金Research Grants Council Joint Research Scheme(62361166630)Guangdong Basic and Applied Basic Research Foundation(2023B1515130007).
文摘Human skin exhibits a remarkable capability to perceive contact forces and environmental temperatures,providing complex information that is essential for its subtle control.Despite recent advancements in soft tactile sensors,accurately decoupling signals—specifically separating forces from directional orientation and temperature—remains a challenge thus resulting in failure to meet the advanced application requirements of robots.This study proposes,F3T,a multilayer soft sensor unit designed to achieve isolated measurements and mathematical decoupling of normal pressure,omnidirectional tangential forces,and temperature.We developed a circular coaxial magnetic film featuring a floating mount multilayer capacitor that facilitated the physical decoupling of normal and tangential forces in all directions.Additionally,we incorporated an ion gel-based temperature-sensing film into the tactile sensor.The proposed sensor was resilient to external pressures and deformations,and could measure temperature and significantly eliminate capacitor errors induced by environmental temperature changes.In conclusion,our novel design allowed for the decoupled measurement of multiple signals,laying the foundation for advancements in high-level robotic motion control,autonomous decision-making,and task planning.
基金financially supported by the National Key R&D Program of China(Nos.2020YFA0908100 and 2023YFF1204401)Shenzhen Medical Research Fund(No.B2302037)+1 种基金the National Natural Science Foundation of China(Nos.22331003 and 21925102)Beijing National Laboratory for Molecular Sciences(No.BNLMS-CXXM-202006)。
文摘Thermophilic proteins maintain their structure and function at high temperatures,making them widely useful in industrial applications.Due to the complexity of experimental measurements,predicting the melting temperature(T_(m))of proteins has become a research hotspot.Previous methods rely on amino acid composition,physicochemical properties of proteins,and the optimal growth temperature(OGT)of hosts for T_(m)prediction.However,their performance in predicting T_(m)values for thermophilic proteins(T_(m)>60℃)are generally unsatisfactory due to data scarcity.Herein,we introduce T_(m)Pred,a T_(m)prediction model for thermophilic proteins,that combines protein language model,graph convolutional network and Graphormer module.For performance evaluation,T_(m)Pred achieves a root mean square error(RMSE)of 5.48℃,a pearson correlation coefficient(P)of 0.784,and a coefficient of determination(R~2)of 0.613,representing improvements of 19%,15%,and 32%,respectively,compared to the state-of-the-art predictive models like DeepTM.Furthermore,T_(m)Pred demonstrated strong generalization capability on independent blind test datasets.Overall,T_(m)Pred provides an effective tool for the mining and modification of thermophilic proteins by leveraging deep learning.
基金support from the Ministry of Science and Technology of Taiwan(Contract Nos.113-2221-E-011-130-MY2 and 113-2218-E-011-002)the support from Intelligent Manufactur-ing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan.
文摘Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.
基金This work was partially supported by the Shandong Joint Fund of the National Nature Science Foundation of China(U2006228)the National Nature Science Foundation of China(61603244).
文摘Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced problems.To alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current study.In the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity.Moreover,the computational resources can be automatically allocated among all the subspaces.The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced MMOPs.The results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced MMOPs.Also,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically.
基金Supported by in part for this work from the Research Grants Council of the Hong Kong Government and the Shun Hing Institute of Advanced Engineering, Hong Kong
文摘The terahertz band lies between the microwave and infrared regions of the electromagnetic spectrum.This radiation has very low photon energy and thus it does not pose any ionization hazard for biological tissues.It is strongly attenuated by water and very sensitive to water content.Unique absorption spectra due to intermolecular vibrations in this region have been found in different biological materials.These unique features make tera-hertz imaging very attractive for medical applications in order to provide complimentary information to existing imaging techniques.There has been an increasing interest in terahertz imaging and spectroscopy of biologically related applications within the last few years and more and more terahertz spectra are being reported.This paper introduces terahertz technology and provides a short review of recent advances in terahertz imaging and spectroscopy techniques,and a number of applications such as molecular spectroscopy,tissue characterization and skin imaging are discussed.
基金Project supported by an Australian Research Council Future Fellowship Grant
文摘In recent years, there have been a significant number of demonstrations of small metallic and plasmonic lasers. The vast majority of these demonstrations have been for optically pumped devices. Electrically pumped devices are advantageous for applications and could demonstrate concepts not amenable for optical pumping. However, there have been relatively few demonstrations of electrically pumped small metal cavity lasers. This lack of results is due to the following reasons： there are limited types of electrically pumped gain media available; there is a significantly greater level of complexity required in the fabrication of electrically pumped devices; finally, the required components for electrical pumping restrict cavity design options and furthermore make it intrinsically more difficult to achieve lasing. This review looks at the motivation for electrically pumped nanolasers, the key issues that need addressing for them to be realized, the results that have been achieved so far including devices where lasing has not been achieved, and potential new directions that could be pursued.
基金the National Basic Research Program of China(Grant No.2013CBA01604)the National Science and Technology Major Project of China(Grant No.2011ZX02707)
文摘Poly(methyl methacrylate)(PMMA) is widely used for graphene transfer and device fabrication.However,it inevitably leaves a thin layer of polymer residues after acetone rinsing and leads to dramatic degradation of device performance.How to eliminate contamination and restore clean surfaces of graphene is still highly demanded.In this paper,we present a reliable and position-controllable method to remove the polymer residues on graphene films by laser exposure.Under proper laser conditions,PMMA residues can be substantially reduced without introducing defects to the underlying graphene.Furthermore,by applying this laser cleaning technique to the channel and contacts of graphene fieldeffect transistors(GFETs),higher carrier mobility as well as lower contact resistance can be realized.This work opens a way for probing intrinsic properties of contaminant-free graphene and fabricating high-performance GFETs with both clean channel and intimate graphene/metal contact.
基金supported by National Natural Science Foundation of China(Project No.51672231)Shen Zhen Science and Technology Innovation Commission(Project No.JCYJ20170818114107730)+1 种基金Hong Kong Research Grant Council(General Research Fund Project Nos.16237816,16309018)the support from the Center for 1D/2D Quantum Materials and the State Key Laboratory on Advanced Displays and Optoelectronics at HKUST
文摘An effective and low-cost front-side anti-reflection(AR) technique has long been sought to enhance the performance of highly efficient photovoltaic devices due to its capability of maximizing the light absorption in photovoltaic devices. In order to achieve high throughput fabrication of nanostructured flexible and anti-reflection films, large-scale, nano-engineered wafer molds were fabricated in this work. Additionally, to gain in-depth understanding of the optical and electrical performance enhancement with AR films on polycrystalline Si solar cells, both theoretical and experimental studies were performed. Intriguingly,the nanocone structures demonstrated an efficient light trapping effect which reduced the surface reflection of a solar cell by17.7% and therefore enhanced the overall electric output power of photovoltaic devices by 6% at normal light incidence. Notably, the output power improvement is even more significant at a larger light incident angle which is practically meaningful for daily operation of solar panels. The application of the developed AR films is not only limited to crystalline Si solar cells explored here, but also compatible with any types of photovoltaic technology for performance enhancement.
基金supported by National Research Foundation of South Africa(UID85783)the National Hub for Energy Efficiency and Demand Side Management and Exxaro
文摘The efficiency of any energy system can be charaterised by the relevant efficiency components in terms of performance, operation, equipment and technology(POET). The overall energy efficiency of the system can be optimised by studying the POET energy efficiency components. For an existing energy system, the improvement of operation efficiency will usually be a quick win for energy efficiency. Therefore, operation efficiency improvement will be the main purpose of this paper. General procedures to establish operation efficiency optimisation models are presented. Model predictive control, a popular technique in modern control theory, is applied to solve the obtained energy models. From the case studies in water pumping systems, model predictive control will have a prosperous application in more energy efficiency problems.
