This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃...This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.展开更多
2025 marks the 30th anniversary of nanoimprint lithography(NIL).Since its inception in 1995,and through global efforts over the past three decades,nanoimprint has emerged as the primary alternative to extreme ultravio...2025 marks the 30th anniversary of nanoimprint lithography(NIL).Since its inception in 1995,and through global efforts over the past three decades,nanoimprint has emerged as the primary alternative to extreme ultraviolet(EUV)lithography for deep-nanoscale silicon(Si)electronics.Numerous semiconductor companies have recognized NIL's manufacturing quality and are actively being evaluated for the production of the most advanced semiconductor devices.Nanoimprinting's potential extends beyond silicon chip fabrication and wafer-scale applica-tions.With its high throughput and 3D patterning capabilities,NIL is becoming a key technology for fabricating emerging devices,such as flat optics and augmented reality glasses.This review summarizes the key developments and applications of nanoimprint lithography,with a particular focus on the latest industry advancements in nano-Si device manufacturing and nanophotonics applications.展开更多
Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance b...Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains challenging.Methods This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and VR.Therefore,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image graph.Using this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of views.The rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image graph.The detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in real-time.In addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light intensities.Results Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,respectively.While achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering approaches.Conclusions Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR environments.Nevertheless,the handling of morphing artifacts in the parallax image regions remains a topic for future research.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration stability.To investigate CO_(2)sequestration dynamics,comprehens...Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration stability.To investigate CO_(2)sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after experimentation.Simultaneously,a method using NMR T2spectra changes to measure pore volume shift and estimate CO_(2)sequestration is introduced.It quantifies CO_(2)needed for mineralization of soluble minerals.However,when CO_(2)dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage capacities.Notably,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller pores.As pressure levels rise,the magnitude of pore alterations progressively increases.At a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05%for small pores.The observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO_(2)sequestration parameters and optimizing strategies in low permeability reservoirs.展开更多
Advancements in sensor technology have significantly enhanced atmospheric monitoring.Notably,metal oxide and carbon(MO_(x)/C)hybrids have gained attention for their exceptional sensitivity and room-temperature sensing...Advancements in sensor technology have significantly enhanced atmospheric monitoring.Notably,metal oxide and carbon(MO_(x)/C)hybrids have gained attention for their exceptional sensitivity and room-temperature sensing performance.However,previous methods of synthesizing MO_(x)/C composites suffer from problems,including inhomogeneity,aggregation,and challenges in micropatterning.Herein,we introduce a refined method that employs a metal–organic framework(MOF)as a precursor combined with direct laser writing.The inherent structure of MOFs ensures a uniform distribution of metal ions and organic linkers,yielding homogeneous MO_(x)/C structures.The laser processing facilitates precise micropatterning(<2μm,comparable to typical photolithography)of the MO_(x)/C crystals.The optimized MOF-derived MO_(x)/C sensor rapidly detected ethanol gas even at room temperature(105 and 18 s for response and recovery,respectively),with a broad range of sensing performance from 170 to 3,400 ppm and a high response value of up to 3,500%.Additionally,this sensor exhibited enhanced stability and thermal resilience compared to previous MOF-based counterparts.This research opens up promising avenues for practical applications in MOF-derived sensing devices.展开更多
The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gai...The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gait in a virtual environment was presented in previous research work titled “A Comparison of PPO, TD3, and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation”. We demonstrated that the Soft Actor-Critic Reinforcement algorithm had the best performance generating the walking gait for a quadruped in certain instances of sensor configurations in the virtual environment. In this work, we present the performance analysis of the state-of-the-art Deep Reinforcement algorithms above for quadruped walking gait generation in a physical environment. The performance is determined in the physical environment by transfer learning augmented by real-time reinforcement learning for gait generation on a physical quadruped. The performance is analyzed on a quadruped equipped with a range of sensors such as position tracking using a stereo camera, contact sensing of each of the robot legs through force resistive sensors, and proprioceptive information of the robot body and legs using nine inertial measurement units. The performance comparison is presented using the metrics associated with the walking gait: average forward velocity (m/s), average forward velocity variance, average lateral velocity (m/s), average lateral velocity variance, and quaternion root mean square deviation. The strengths and weaknesses of each algorithm for the given task on the physical quadruped are discussed.展开更多
We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures...We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these approaches.Combustion synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to patterning.Thermal analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous phase.Furthermore,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen networks.RRAM devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature annealing.Combustion synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.展开更多
The main objective of this study is estimating environmental pollution of hybrid biomass and co-generation power plants. Efficiency of direct tapping of biomass is about 15%-20%. Consequently, about 80% of energy woul...The main objective of this study is estimating environmental pollution of hybrid biomass and co-generation power plants. Efficiency of direct tapping of biomass is about 15%-20%. Consequently, about 80% of energy would be waste in this method. While in co-generation power plant, this number could improve to more than 50%. Therefore, to achieve higher efficiency in utilizing biomass energy, co-generation power plants is proposed by using biogas as fuel instead of natural gas. Proposed system would be supplied thermal and electrical energy for non-urban areas of Iran. In this regard, process of fermentation and gas production from biomass in a vertical digester is studied and simulated using analytic methods. Various factors affecting the fermentation, such as temperature, humidity, PH and optimal conditions for the extraction of gas from waste agriculture and animal are also determined. Comparing between the pollution emission from fossil fuel power plants and power plants fed by biomass shows about 88% reduction in greenhouse emission which significant number.展开更多
The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its p...The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.展开更多
Deep learning for time series sequence individual data instance classification can revolutionize computer assisted navigation by providing surgeons with accurate, real-time instrument locality through automatic instru...Deep learning for time series sequence individual data instance classification can revolutionize computer assisted navigation by providing surgeons with accurate, real-time instrument locality through automatic instrument localization. This paper presents an evaluation of Deep Learning models to perform individual data instance classification of time series data. The models explored include convolution and recurrent networks, as well as state-of-the-art residual and inception architectures. The time series data used to evaluate the models consists of depth and force measurements from a drill. Four recurrent neural network models using long short-term memory and gated recurrent units, known as baseline models, and four models using 1D convolution with ResNet and Inception architectures, known as advanced models, were evaluated by determining the data instance membership of the four classes. The four classes represent four distinct regions in a bone traversed by the drill bit during a surgical procedure. First, the time series data is preprocessed, identifying the four classes or regions of the bone. Next, the paper presents a discussion of the network architecture and modifications of both the basic and advanced deep learning models, followed by the training process and hyperparameters tuning. The performance of the models was evaluated using the precision and recall performance parameters. Out of the eight models evaluated, the recurrent neural network with gated recurrent units has the best performance. The paper also demonstrates the importance of the feature depth over the feature force in classifying the data instances, followed by the effects of the imbalanced dataset on the performance of the models.展开更多
This paper investigates the impact of reducing feature-vector dimensionality on the performance of machine learning(ML)models.Dimensionality reduction and feature selection techniques can improve computational efficie...This paper investigates the impact of reducing feature-vector dimensionality on the performance of machine learning(ML)models.Dimensionality reduction and feature selection techniques can improve computational efficiency,accuracy,robustness,transparency,and interpretability of ML models.In high-dimensional data,where features outnumber training instances,redundant or irrelevant features introduce noise,hindering model generalization and accuracy.This study explores the effects of dimensionality reduction methods on binary classifier performance using network traffic data for cybersecurity applications.The paper examines how dimensionality reduction techniques influence classifier operation and performance across diverse performancemetrics for seven ML models.Four dimensionality reduction methods are evaluated:principal component analysis(PCA),singular value decomposition(SVD),univariate feature selection(UFS)using chi-square statistics,and feature selection based on mutual information(MI).The results suggest that direct feature selection can be more effective than data projection methods in some applications.Direct selection offers lower computational complexity and,in some cases,superior classifier performance.This study emphasizes that evaluation and comparison of binary classifiers depend on specific performance metrics,each providing insights into different aspects of ML model operation.Using open-source network traffic data,this paper demonstrates that dimensionality reduction can be a valuable tool.It reduces computational overhead,enhances model interpretability and transparency,and maintains or even improves the performance of trained classifiers.The study also reveals that direct feature selection can be a more effective strategy when compared to feature engineering in specific scenarios.展开更多
In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance syst...In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.展开更多
A machine learning model, using the transformer architecture, is used to design a feedback compensator and prefilter for various simulated plants. The output of the transformer is a sequence of compensator and prefilt...A machine learning model, using the transformer architecture, is used to design a feedback compensator and prefilter for various simulated plants. The output of the transformer is a sequence of compensator and prefilter parameters. The compensator and prefilter are linear models, preserving the ability to analyze the system with linear control theory. The input to the network is a window of recent reference and output samples. The goal of the transformer is to minimize tracking error at each time step. The plants under consideration range from simple to challenging. The more difficult plants contain closely spaced, lightly damped, complex conjugate pairs of poles and zeros. Results are compared to PID controllers tuned for a similar crossover frequency and optimal phase margin. For simple plants, the transformer converges to solutions which overly rely on the prefilter, neglecting the maximization of negative feedback. For more complex plants, the transformer designs a compensator and prefilter with more desirable qualities. In all cases, the transformer can start with random model parameters and modify them to minimize tracking error on the step reference.展开更多
This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential p...This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.展开更多
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec...Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.展开更多
Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission sche...Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is proposed.To model CSI uncertainty,an expectation-based error model is utilized.The main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model aggregation.The problem is formulated as a combinatorial optimization problem and is solved in two steps.First,the priority order of devices is determined by a sparsity-inducing procedure.Then,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met.An alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex subproblems.Numerical results illustrate the effectiveness and robustness of the proposed scheme.展开更多
The evolution of display backplane technologies has been driven by the relentless pursuit of higher form factor and superior performance coupled with lower power consumption.Current state-of-the-art backplane technolo...The evolution of display backplane technologies has been driven by the relentless pursuit of higher form factor and superior performance coupled with lower power consumption.Current state-of-the-art backplane technologies based on amorphous Si,poly Si,and IGZO,face challenges in meeting the requirements of next-generation displays,including larger dimensions,higher refresh rates,increased pixel density,greater brightness,and reduced power consumption.In this context,2D chalcogenides have emerged as promising candidates for thin-film transistors(TFTs)in display backplanes,offering advantages such as high mobility,low leakage current,mechanical robustness,and transparency.This comprehensive review explores the significance of 2D chalcogenides as materials for TFTs in next-generation display backplanes.We delve into the structural characteristics,electronic properties,and synthesis methods of 2D chalcogenides,emphasizing scalable growth strategies that are relevant to large-area display backplanes.Additionally,we discuss mechanical flexibility and strain engineering,crucial for the development of flexible displays.Performance enhancement strategies for 2D chalcogenide TFTs have been explored encompassing techniques in device engineering and geometry optimization,while considering scaling over a large area.Active-matrix implementation of 2D TFTs in various applications is also explored,benchmarking device performance on a large scale which is a necessary aspect of TFTs used in display backplanes.Furthermore,the latest development on the integration of 2D chalcogenide TFTs with different display technologies,such as OLED,quantum dot,and MicroLED displays has been reviewed in detail.Finally,challenges and opportunities in the field are discussed with a brief insight into emerging trends and research directions.展开更多
Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrat...Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.展开更多
Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we deve...Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.展开更多
文摘This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.
基金the National Science Foundation for the partial support(NSF-2213684),and LJG acknowledges an Emmett Leith Collegiate Professorship for this writing.
文摘2025 marks the 30th anniversary of nanoimprint lithography(NIL).Since its inception in 1995,and through global efforts over the past three decades,nanoimprint has emerged as the primary alternative to extreme ultraviolet(EUV)lithography for deep-nanoscale silicon(Si)electronics.Numerous semiconductor companies have recognized NIL's manufacturing quality and are actively being evaluated for the production of the most advanced semiconductor devices.Nanoimprinting's potential extends beyond silicon chip fabrication and wafer-scale applica-tions.With its high throughput and 3D patterning capabilities,NIL is becoming a key technology for fabricating emerging devices,such as flat optics and augmented reality glasses.This review summarizes the key developments and applications of nanoimprint lithography,with a particular focus on the latest industry advancements in nano-Si device manufacturing and nanophotonics applications.
基金Supported by the Bavarian Academic Forum(BayWISS),as a part of the joint academic partnership digitalization program.
文摘Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains challenging.Methods This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and VR.Therefore,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image graph.Using this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of views.The rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image graph.The detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in real-time.In addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light intensities.Results Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,respectively.While achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering approaches.Conclusions Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR environments.Nevertheless,the handling of morphing artifacts in the parallax image regions remains a topic for future research.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
基金support of the National Natural Science Foundation of China(Grant Nos.52174030,52474042 and 52374041)the Postgraduate Innovation Fund Project of Xi'an Shiyou University(No.YCX2411001)the Natural Science Basic Research Program of Shaanxi(Program Nos.2024JCYBMS-256 and 2022JQ-528)。
文摘Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration stability.To investigate CO_(2)sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after experimentation.Simultaneously,a method using NMR T2spectra changes to measure pore volume shift and estimate CO_(2)sequestration is introduced.It quantifies CO_(2)needed for mineralization of soluble minerals.However,when CO_(2)dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage capacities.Notably,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller pores.As pressure levels rise,the magnitude of pore alterations progressively increases.At a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05%for small pores.The observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO_(2)sequestration parameters and optimizing strategies in low permeability reservoirs.
基金supported by the National Research Foundation of Korea(NRF)grants funded by the Ministry of Science and ICT(MSIT)(RS-2023-00251283,and 2022M3D1A2083618)by the Ministry of Education(2020R1A6A1A03040516).
文摘Advancements in sensor technology have significantly enhanced atmospheric monitoring.Notably,metal oxide and carbon(MO_(x)/C)hybrids have gained attention for their exceptional sensitivity and room-temperature sensing performance.However,previous methods of synthesizing MO_(x)/C composites suffer from problems,including inhomogeneity,aggregation,and challenges in micropatterning.Herein,we introduce a refined method that employs a metal–organic framework(MOF)as a precursor combined with direct laser writing.The inherent structure of MOFs ensures a uniform distribution of metal ions and organic linkers,yielding homogeneous MO_(x)/C structures.The laser processing facilitates precise micropatterning(<2μm,comparable to typical photolithography)of the MO_(x)/C crystals.The optimized MOF-derived MO_(x)/C sensor rapidly detected ethanol gas even at room temperature(105 and 18 s for response and recovery,respectively),with a broad range of sensing performance from 170 to 3,400 ppm and a high response value of up to 3,500%.Additionally,this sensor exhibited enhanced stability and thermal resilience compared to previous MOF-based counterparts.This research opens up promising avenues for practical applications in MOF-derived sensing devices.
文摘The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gait in a virtual environment was presented in previous research work titled “A Comparison of PPO, TD3, and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation”. We demonstrated that the Soft Actor-Critic Reinforcement algorithm had the best performance generating the walking gait for a quadruped in certain instances of sensor configurations in the virtual environment. In this work, we present the performance analysis of the state-of-the-art Deep Reinforcement algorithms above for quadruped walking gait generation in a physical environment. The performance is determined in the physical environment by transfer learning augmented by real-time reinforcement learning for gait generation on a physical quadruped. The performance is analyzed on a quadruped equipped with a range of sensors such as position tracking using a stereo camera, contact sensing of each of the robot legs through force resistive sensors, and proprioceptive information of the robot body and legs using nine inertial measurement units. The performance comparison is presented using the metrics associated with the walking gait: average forward velocity (m/s), average forward velocity variance, average lateral velocity (m/s), average lateral velocity variance, and quaternion root mean square deviation. The strengths and weaknesses of each algorithm for the given task on the physical quadruped are discussed.
基金supported by the National Research Founda-tion of Korea(NRF)grants funded by the Ministry of Science and ICT(MSIT)(Nos.RS-2023-00251283,RS-2023-00257003,and 2022M3D1A2083618)supported by the DGIST R&D Program of the MSIT(No.23-CoE-BT-03).
文摘We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these approaches.Combustion synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to patterning.Thermal analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous phase.Furthermore,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen networks.RRAM devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature annealing.Combustion synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.
文摘The main objective of this study is estimating environmental pollution of hybrid biomass and co-generation power plants. Efficiency of direct tapping of biomass is about 15%-20%. Consequently, about 80% of energy would be waste in this method. While in co-generation power plant, this number could improve to more than 50%. Therefore, to achieve higher efficiency in utilizing biomass energy, co-generation power plants is proposed by using biogas as fuel instead of natural gas. Proposed system would be supplied thermal and electrical energy for non-urban areas of Iran. In this regard, process of fermentation and gas production from biomass in a vertical digester is studied and simulated using analytic methods. Various factors affecting the fermentation, such as temperature, humidity, PH and optimal conditions for the extraction of gas from waste agriculture and animal are also determined. Comparing between the pollution emission from fossil fuel power plants and power plants fed by biomass shows about 88% reduction in greenhouse emission which significant number.
文摘The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.
文摘Deep learning for time series sequence individual data instance classification can revolutionize computer assisted navigation by providing surgeons with accurate, real-time instrument locality through automatic instrument localization. This paper presents an evaluation of Deep Learning models to perform individual data instance classification of time series data. The models explored include convolution and recurrent networks, as well as state-of-the-art residual and inception architectures. The time series data used to evaluate the models consists of depth and force measurements from a drill. Four recurrent neural network models using long short-term memory and gated recurrent units, known as baseline models, and four models using 1D convolution with ResNet and Inception architectures, known as advanced models, were evaluated by determining the data instance membership of the four classes. The four classes represent four distinct regions in a bone traversed by the drill bit during a surgical procedure. First, the time series data is preprocessed, identifying the four classes or regions of the bone. Next, the paper presents a discussion of the network architecture and modifications of both the basic and advanced deep learning models, followed by the training process and hyperparameters tuning. The performance of the models was evaluated using the precision and recall performance parameters. Out of the eight models evaluated, the recurrent neural network with gated recurrent units has the best performance. The paper also demonstrates the importance of the feature depth over the feature force in classifying the data instances, followed by the effects of the imbalanced dataset on the performance of the models.
基金funded by US Army Combat Capabilities Development Command(CCDC)Aviation&Missile Center,https://www.avmc.army.mil/(accessed on 5 February 2024),CONTRACT NUMBER:W31P4Q-18-D-0002 through Georgia Tech Research Institute and AAMU-RISE。
文摘This paper investigates the impact of reducing feature-vector dimensionality on the performance of machine learning(ML)models.Dimensionality reduction and feature selection techniques can improve computational efficiency,accuracy,robustness,transparency,and interpretability of ML models.In high-dimensional data,where features outnumber training instances,redundant or irrelevant features introduce noise,hindering model generalization and accuracy.This study explores the effects of dimensionality reduction methods on binary classifier performance using network traffic data for cybersecurity applications.The paper examines how dimensionality reduction techniques influence classifier operation and performance across diverse performancemetrics for seven ML models.Four dimensionality reduction methods are evaluated:principal component analysis(PCA),singular value decomposition(SVD),univariate feature selection(UFS)using chi-square statistics,and feature selection based on mutual information(MI).The results suggest that direct feature selection can be more effective than data projection methods in some applications.Direct selection offers lower computational complexity and,in some cases,superior classifier performance.This study emphasizes that evaluation and comparison of binary classifiers depend on specific performance metrics,each providing insights into different aspects of ML model operation.Using open-source network traffic data,this paper demonstrates that dimensionality reduction can be a valuable tool.It reduces computational overhead,enhances model interpretability and transparency,and maintains or even improves the performance of trained classifiers.The study also reveals that direct feature selection can be a more effective strategy when compared to feature engineering in specific scenarios.
文摘In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.
文摘A machine learning model, using the transformer architecture, is used to design a feedback compensator and prefilter for various simulated plants. The output of the transformer is a sequence of compensator and prefilter parameters. The compensator and prefilter are linear models, preserving the ability to analyze the system with linear control theory. The input to the network is a window of recent reference and output samples. The goal of the transformer is to minimize tracking error at each time step. The plants under consideration range from simple to challenging. The more difficult plants contain closely spaced, lightly damped, complex conjugate pairs of poles and zeros. Results are compared to PID controllers tuned for a similar crossover frequency and optimal phase margin. For simple plants, the transformer converges to solutions which overly rely on the prefilter, neglecting the maximization of negative feedback. For more complex plants, the transformer designs a compensator and prefilter with more desirable qualities. In all cases, the transformer can start with random model parameters and modify them to minimize tracking error on the step reference.
文摘This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.
文摘Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.
文摘Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is proposed.To model CSI uncertainty,an expectation-based error model is utilized.The main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model aggregation.The problem is formulated as a combinatorial optimization problem and is solved in two steps.First,the priority order of devices is determined by a sparsity-inducing procedure.Then,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met.An alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex subproblems.Numerical results illustrate the effectiveness and robustness of the proposed scheme.
基金supported in part by the National Research Foundation of Korea Grant Number:RS-2024-00448809National Research Foundation of Korea Grant Number:RS-2025-00517255+1 种基金National Research Foundation of Korea Grant Number:No.2021M3H4A1A02056037supported by Basic Science Research Program through the National Research Foundation of Korean(NRF)funded by the Ministry of Education(2020R1A6A1A03040516).
文摘The evolution of display backplane technologies has been driven by the relentless pursuit of higher form factor and superior performance coupled with lower power consumption.Current state-of-the-art backplane technologies based on amorphous Si,poly Si,and IGZO,face challenges in meeting the requirements of next-generation displays,including larger dimensions,higher refresh rates,increased pixel density,greater brightness,and reduced power consumption.In this context,2D chalcogenides have emerged as promising candidates for thin-film transistors(TFTs)in display backplanes,offering advantages such as high mobility,low leakage current,mechanical robustness,and transparency.This comprehensive review explores the significance of 2D chalcogenides as materials for TFTs in next-generation display backplanes.We delve into the structural characteristics,electronic properties,and synthesis methods of 2D chalcogenides,emphasizing scalable growth strategies that are relevant to large-area display backplanes.Additionally,we discuss mechanical flexibility and strain engineering,crucial for the development of flexible displays.Performance enhancement strategies for 2D chalcogenide TFTs have been explored encompassing techniques in device engineering and geometry optimization,while considering scaling over a large area.Active-matrix implementation of 2D TFTs in various applications is also explored,benchmarking device performance on a large scale which is a necessary aspect of TFTs used in display backplanes.Furthermore,the latest development on the integration of 2D chalcogenide TFTs with different display technologies,such as OLED,quantum dot,and MicroLED displays has been reviewed in detail.Finally,challenges and opportunities in the field are discussed with a brief insight into emerging trends and research directions.
基金supported by the National Key R&D Program of China(Grant No.2021YFA1001000)the National Natural Science Foundation of China(Grant Nos.82111530212,U23A20282,and 61971255)+2 种基金the Natural Science Founda-tion of Guangdong Province(Grant No.2021B1515020092)the Shenzhen Bay Laboratory Fund(Grant No.SZBL2020090501014)the Shenzhen Science,Technology and Innovation Commission(Grant Nos.KJZD20231023094659002,JCYJ20220530142809022,and WDZC20220811170401001).
文摘Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.
基金supported in part by the National Nature Science Fundation(61174009,61203323)Youth Foundation of Hebei Province(F2016202327)+3 种基金the Colleges and Universities in Hebei Province Science and Technology Research Project(ZC2016020)supported in part by Key Project of NSFC(61533009)111 Project(B08015)Research Project(JCYJ20150403161923519)
文摘Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.
