Fast Feature Research Articles

Proton and Redox Couple Synergized Strategy for Aqueous Low Voltage-Driven WO3 Electrochromic Devices.

Aqueous electrolytes possess non-combustible and eco-friendly features compared to organic electrolytes, leading them to be more suitable for application in smart windows for daily use. However, limited by the narrow electrochemical window of water (1.23 V), its use in conventional electrochromic devices (ECDs) would result in irreversible performance loss, which arises from decomposition caused by high voltage. Here, we propose a synergistic scheme combining a redox couple-catalytic counter electrode (RC-CCE) strategy with protons as guest ions. With the help of the intelligent matching of the reaction potentials of the RC and amorphous WO3 electrochromic electrodes and the highly active and fast kinetic features of protons, it successfully reduces the working voltage range of the device to 1.1 V. The assembled HClO4-ECD can possess an overall modulation rate (350-1200 nm) of 0.43 and 0.94 at -0.1 and -0.7 V, respectively, and a modulation of 66.8% at 600 nm at -0.7 V. Moreover, compared with other guest ions, the proton-based ECD exhibits higher coloration efficiency, a broader color modulation capability, and better stability. In addition, the house model equipped with the proton-based ECD effectively blocks solar radiation, which provides a potential solution for the design of aqueous smart windows.

Current research and progress of machine learning in traffic monitoring systems

In modern traffic environments and road construction, road monitoring is one of the most important safeguards for urban safety. Traditional image processing techniques are not suitable for comprehensive data collection and thus accurate and fast feature extraction and generalization of the massive amount of data generated. Real-time surveillance is both tedious and tiring, but vital to safety. This paper describes the traditional image and video processing techniques that have been used to monitor pavements, as well as todays means of image processing for pavement inspection, and summarize the current development based on existing literature research. The result shows that the application of these methods can lead to a better understanding of complex pavement conditions, improved road planning and design and overall safety, and even derive applications to other aspects of computer vision as well.

Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors.

Automated forest machines are becoming important due to human operators' complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.

Feature aggregation with transformer for RGB-T salient object detection

The main purpose of RGB-T salient object detection (SOD) is to fully integrate and exploit the information from the complementary fusion of modalities to address the underperformance of RGB SOD in some challenging scenes. In this paper, we propose a novel feature aggregation network that can fully mine multi-scale and multi-modal information for complete and accurate RGB-T SOD. Subsequently, a cross-attention fusion module is proposed to adaptively integrate high-level features by using the attention mechanism in the Transformer. Then we design a simple yet effective fast feature aggregation module to fuse low-level features. Through the combined work of the above modules, our network can perform well in some complex scenes by effectively fusing features from RGB and thermal modalities. Finally, several experiments on publicly available datasets such as VT821, VT1000, and VT5000 demonstrate that our method outperforms state-of-the-art methods. And our code has been released at:https://github.com/ELOESZHANG/FANet.

Measurement of three-dimensional shrinkage deformations and volumes of stabilised soft clay during drying with Structure from Motion photogrammetry

The paper applies the Structure from Motion photogrammetry technique to measure three-dimensional shrinkage deformations of a cylindrical specimen during drying. A clay–binder mixture was statically compacted, and its shrinkage behaviour was observed for four weeks with a newly established photogrammetric system. The complete surface information including XYZ coordinates and RGB values was then reconstructed and formed into dense point cloud data, with which the volume change and shrinkage deformations are computed. In volume change determination, the reconstructed dense point cloud achieves a fine reproduction of the specimen surface, leading to satisfactory accuracy. The computed volume deviation was within 495.3 mm3 (0.24%) and 4170 mm3 (1.83%) for the dummy cylinder and clay specimen, respectively. Both values are significantly smaller than the volume deviations computed with a classical approach that involves artificially creating markers on the specimen surface. In the computation of shrinkage deformations, the paper proposes a novel method that detects the fast point feature histogram of the point clouds and then matches the detected descriptors, leading to an improvement in three-dimensional deformation determination. The compacted sample shrinks, and the computed axial deformations increase quasi-linearly with height. However, due to the noticeable tilt that occurred during drying, the calculated radial deformations are scattered at the same height and appear misleading. Circumferential deformations and strains are therefore adopted to represent the true material behaviour.

Adaptive feature selection for image steganalysis based on classification metrics

Feature selection can remove redundant and useless features, which is an essential way to improve steganalysis efficiency. However, with the diversity of steganalysis features, feature selection has run into bottlenecks of high time cost, poor universality, and experience depending on parameter setting. To this end, an adaptive steganalytic feature selection based classification metrics is proposed. First, the categories to which the features belong are redefined and classified. Secondly, three metrics are proposed for three different categories of features, to make the metric more precise. Then, to reduce the computational cost and optimize parameters, two adaptive threshold models are designed, which achieve the purpose of fast and effective feature selection without relying on the time-consuming classification results. Experimental results on 11 typical steganalytic features demonstrate that compared with classic and state-of-the-art feature selection methods, the proposed method achieves competitive performance on detection accuracy, calculation cost, storage cost, and universality.

Video‐based fast image set classification forIoTmonitoring system

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. And the video monitoring system is an important basic work in the IoT system. However, many existing video monitoring system usually use feature embedding method to learn more discriminative feature representation, while this manner is very time‐consuming. And the learned features usually do not match the subsequent classifiers. To solve these issues, this paper proposes a new video‐based fast image set classification framework, which consists of fast feature learning part and representation learning based classifier part. Extensive experiments on several well‐known benchmark datasets demonstrate the effectiveness and efficiency of the proposed framework.

An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion

Point cloud completion is a necessary task in real-world applications of recovering a complete geometry from missing regions of 3D objects. Furthermore, model efficiency is of vital importance in computer vision. In this paper, we present an efficient encoder–decoder network that predicts missing point clouds on the basis of incomplete point clouds. There are several advantages to this approach. First, a Mixed Attention Module (MAM) was implemented to obtain the correlational information of points. Second, the proposed Bidirectional Point Pyramid Attention Network (BiPPAN) can achieve simple and fast multiscale feature fusion to capture important features. Lastly, the designed encoder–decoder framework comprises skip connections to capture long-distance dependencies and structural information. We can conclude from the results of the experiments that the proposed network is an efficient and effective method to accomplish point cloud completion tasks.

A Novel Sulfonamide, Molecularly Imprinted, Upconversion Fluorescence Probe Prepared by Pickering Emulsion Polymerization and Its Adsorption and Optical Sensing Performance.

A novel, molecularly imprinted, upconversion fluorescence probe (UCNP@MIFP) for sulfonamide sensing was fabricated by Pickering emulsion polymerization using UCNP@SiO2 particles as the stabilizer and sulfamethazine/sulfamerazine as the co-templates. The synthesis conditions of the UCNP@MIFP were optimized, and the synthesized probe was characterized by scanning electron microscopy, Fourier transform infrared spectrometer, thermogravimetric analyzer, and fluorescence spectrometer. The UCNP@MIFPs showed a good adsorption capacity and a fast kinetic feature for the template. The selectivity experiment revealed that the UCNP@MIFP has a broad-spectrum molecular recognition capability. Good linear relationships were obtained over the concentration range of 1-10 ng/mL for sulfamerazine, sulfamethazine, sulfathiazole, and sulfafurazole, with low limits of detection in the range of 1.37-2.35 ng/mL. The prepared UCNP@MIFP has the potential to detect four sulfonamide residues in food and environmental water.

A Novel Fuzzy C-Means based Chameleon Swarm Algorithm for Segmentation and Progressive Neural Architecture Search for Plant Disease Classification

This study proposed a novel framework for plant leaf disease identification. The proposed model consists of four steps including pre-processing, segmentation, feature extraction, and classification. At first, the unwanted noise and overfitting are removed, and also image contrast level is enhanced. Secondly, the Fuzzy C-Means (FCM) based Chameleon Swarm Algorithm (CSA) named as (FCM-CSA) is used for plant leaf diseased part segmentation. In the third stage, the feature extraction is performed using a fast GLCM feature extraction model. Finally, the Progressive Neural Architecture Search (PNAS) is used for plant leaf disease identification. The experimental investigations are carried out using MATLAB software with the Mendeley database. From this dataset, we have used Apple Cedar Apple Rust (ACAR), Cherry Powdery Mildew (CPM), Corn Common Rust (CCCR), Apple Healthy (AH), Grape Black Rot (GBR), Pepper Bell Bacterial Spot (PBBS), Potato Late Blight (PLB) and Tomato Leaf Mold (TLM) disease images. Different measures such as precision, recall, sensitivity, specificity, and accuracy results are used to validate the performance of the proposed model.

Synchrony-Division Neural Multiplexing: An Encoding Model

Cortical neurons receive mixed information from the collective spiking activities of primary sensory neurons in response to a sensory stimulus. A recent study demonstrated an abrupt increase or decrease in stimulus intensity and the stimulus intensity itself can be respectively represented by the synchronous and asynchronous spikes of S1 neurons in rats. This evidence capitalized on the ability of an ensemble of homogeneous neurons to multiplex, a coding strategy that was referred to as synchrony-division multiplexing (SDM). Although neural multiplexing can be conceived by distinct functions of individual neurons in a heterogeneous neural ensemble, the extent to which nearly identical neurons in a homogeneous neural ensemble encode multiple features of a mixed stimulus remains unknown. Here, we present a computational framework to provide a system-level understanding on how an ensemble of homogeneous neurons enable SDM. First, we simulate SDM with an ensemble of homogeneous conductance-based model neurons receiving a mixed stimulus comprising slow and fast features. Using feature-estimation techniques, we show that both features of the stimulus can be inferred from the generated spikes. Second, we utilize linear nonlinear (LNL) cascade models and calculate temporal filters and static nonlinearities of differentially synchronized spikes. We demonstrate that these filters and nonlinearities are distinct for synchronous and asynchronous spikes. Finally, we develop an augmented LNL cascade model as an encoding model for the SDM by combining individual LNLs calculated for each type of spike. The augmented LNL model reveals that a homogeneous neural ensemble model can perform two different functions, namely, temporal- and rate-coding, simultaneously.

Compactness score: a fast filter method for unsupervised feature selection

The rapid development of big data era incurs the generation of huge amount of data day by day in various fields. Due to the large-scale and high-dimensional characteristics of these data, it is often difficult to achieve better decision-making in practical applications. Therefore, an efficient big data analytical method is urgently necessary. For feature engineering, feature selection seems to be an important research topic which is anticipated to select “excellent” features from candidate ones. The implementation of feature selection can not only achieve the purpose of dimensionality reduction, but also improve the computational efficiency and result performance of the model. In many classification tasks, researchers found that data seem to be usually close to each other if they are from the same class; thus, local compactness is of great importance for the evaluation of a feature. Based on this discovery, we propose a fast unsupervised feature selection algorithm, named Compactness Score (CSUFS), to select desired features. To prove the superiority of the proposed algorithm, several public data sets are considered with extensive experiments being performed. The experiments are presented by applying feature subsets selected through several different algorithms to the clustering task. The performance of clustering tasks is indicated by two well-known evaluation metrics, while the efficiency is reflected by the corresponding running time. As demonstrated, our proposed algorithm is more accurate and efficient compared with existing ones.

Digital Fringe Projection-Based Clamping Force Estimation Algorithm for Railway Fasteners

The inspection of railway fasteners to assess their clamping force can be used to evaluate the looseness of the fasteners and improve railway safety. Although there are various methods for inspecting railway fasteners, there is still a need for non-contact, fast inspection without installing additional devices on fasteners. In this study, a system that uses digital fringe projection technology to measure the 3D topography of the fastener was developed. This system inspects the looseness through a series of algorithms, including point cloud denoising, coarse registration based on fast point feature histograms (FPFH) features, fine registration based on the iterative closest point (ICP) algorithm, specific region selection, kernel density estimation, and ridge regression. Unlike the previous inspection technology, which can only measure the geometric parameters of fasteners to characterize the tightness, this system can directly estimate the tightening torque and the bolt clamping force. Experiments on WJ-8 fasteners showed a root mean square error of 9.272 N·m and 1.94 kN for the tightening torque and clamping force, demonstrating that the system is sufficiently precise to replace manual measurement and can substantially improve inspection efficiency while evaluating railway fastener looseness.

Spatiotemporal Detection and Localization of Object Removal Video Forgery with Multiple Feature Extraction and Optimized Residual Network

Video forgery detection and localization is one of the most important issue due to the advanced editing software that provides strengthen to tools for manipulating the videos. Object based video tampering destroys the originality of the video. The main aim of the video forensic is to eradicate the forgeries from the original video that are useful in various applications. However, the research on detecting and localizing the object based video forgery with advanced techniques still remains the open and challenging issue. Many of the existing techniques have focused only on detecting the forged video under static background that cannot be applicable for detecting the forgery in tampered video. In addition to this, conventional techniques fail to extract the essential features in order to investigate the depth of the video forgery. Hence, this paper brings a novel technique for detecting and localizing the forged video with multiple features. The steps involved in this research are keyframe extraction, pre-processing, feature extraction and finally detection and localization of forged video. Initially, keyframe extraction uses the Gaussian mixture model (GMM) to extract frames from the forged videos. Then, the pre-processing stage is manipulated to convert the RGB frame into a grayscale image. Multi-features need to be extracted from the pre-processed frames to study the nature of the forged videos. In our proposed study, speeded up robust features (SURF), principal compound analysis histogram oriented gradients (PCA-HOG), model based fast digit feature (MBFDF), correlation of adjacent frames (CAF), the prediction residual gradient (PRG) and optical flow gradient (OFG) features are extracted. The dataset used for the proposed approach is collected from REWIND of about 40 forged and 40 authenticated videos. With the help of the DL approach, video forgery can be detected and localized. Thus, this research mainly focuses on detecting and localization of forged video based on the ResNet152V2 model hybrid with the bidirectional gated recurrent unit (Bi-GRU) to attain maximum accuracy and efficiency. The performance of this approach is finally compared with existing approaches in terms of accuracy, precision, F-measure, sensitivity, specificity, false-negative rate (FNR), false discovery rate (FDR), false-positive rate (FPR), Mathew’s correlation coefficient (MCC) and negative predictive value (NPV). The proposed approach assures the performance of 96.17% accuracy, 96% precision, 96.14% F-measure, 96.58% sensitivity, 96.5% specificity, 0.034 FNR, 0.04 FDR, 0.034 FPR, 0.92 MCC and 96% NPV, respectively. Along with is, the mean square error (MSE) and peak-to-signal-noise ratio (PSNR) for the GMM model attained about 104 and 27.95, respectively.

Multiple stages of smoking phenomenon in electron beam powder bed fusion process

Smoking is a unique phenomenon in the electron beam powder bed fusion (EB-PBF) process. The far-field effect and fast expansion features of smoking often cause the destruction of the powder bed, resulting in part failure or even equipment damage, which seriously restricts the application and development of EB-PBF. However, the generation and development of the “powder cloud” during the smoking process are yet open research questions. This study proposed and established optical & electronic monitoring systems to observe the process of smoking and collect electronic signals to reveal the mechanism behind smoking. Simulation at a small scale where the powder motion of each powder at the affected zone in the powder bed is described by the discrete element method was conducted to simulate the effect of the inertial force and electrostatic force. Observations demonstrated that the smoking process consists of multiple stages. The powder bed showed different motion characteristics and generated different electronic signal patterns at different stages. Numerical simulations revealed that the electrostatic force is the main driving force and confirmed the promoting effect of charged particles striking the powder bed on smoking expansion. The current study provided interpretations of the mechanism as well as a potential solution for real-time monitoring and smoking prevention.

Ionogel electrolyte with dynamic metal-ligand interactions enabled self-healable supercapacitor with high energy density

Flexible semisolid supercapacitors (FSCs) are a kind of essential energy supplies for fast-developed wearable devices. However, the widely reported FSCs use hydrogel electrolytes, dramatically limiting the improvement of the energy density and working potential. Using ionogel electrolytes can solve these problems, but tolerating destruction is still a challenge. Herein, an ionogel electrolyte with good flexibility, high mechanical strength, wide potential window, and fast self-healing feature is designed to assemble the FSCs, solving the above problems simultaneously. The metal-ligand interaction of P(VI-co-VmimBF4) with Zn2+ as the dynamic motif is introduced in the ionogel electrolyte, which not only leads to good flexibility and high mechanical strength of 0.58 MPa, but also endows the fast self-healing ability. Benefitting from the good ionic conductivity of 0.36 mS/cm and wide potential window of 3.2 V of the electrolyte, the assembled FSCs with a covalent organic framework/reduced graphene oxide electrodes achieve a high energy density of 46 Wh/kg. No capacitance deterioration appears for the assembled supercapacitor in bending, and 85% of the original capacitance can be retained for the healed device from two cut pieces. This work provides a way to design a high-performance gel electrolyte for assembling flexible and healable energy storage devices.

Distributionally Robust Decentralized Scheduling Between the Transmission Market and Local Energy Hubs

The cross impacts between transmission and distribution systems have drawn extensive attention, where multi-energy carriers become increasingly dominant in local market operations. Local energy hubs, integrated with multi-energy carriers, e.g., electricity, gas, heat, present great potentials to provide adequate power and reserve support for the transmission system, which could mitigate issues such as boundary mismatches in the upstream market. However, energy hubs are typically equipped with abundant distributed renewable resources. Such local uncertainties can adversely affect the well-being of the coordinated multi-energy market hierarchy. This paper presents a novel multi-period scheduling framework that considers the coordination between the transmission network and distribution energy hubs. Each agent performs local scheduling operations capturing independent uncertainties via a distributionally robust formulation. We then apply a tailored accelerated augmented Lagrangian algorithm embedded with the column-and-constraint method to decentralize the overall operation with agents' privacy preserved. The fast and convergent feature of the algorithm ensures its scalability and reliability in real-world applications of the energy hub design with transmission coordination. Numerical experiments confirm the efficacy of the proposed method.

CoO embedded porous biomass-derived carbon as dual-functional host material for lithium-sulfur batteries

A new strategy is developed to fabricate sulfur electrode by infusing sulfur into a conductive biochar decorated with highly dispersed CoO nanoparticles. The loading of the CoO nanoparticles, as the active sites for reactions, is efficiently increased by using the microwave-assisted diffusion method. It is demonstrated that biochar can serve as an excellent conductive framework to effectively activate sulfur. Simultaneously, the CoO nanoparticles possessing excellent capability to adsorb polysulfides can remarkably alleviate the dissolution of polysulfides, and greatly enhance the conversion kinetics between the polysulfides and Li2S2/Li2S in the charge/discharge processes. The sulfur electrode dual-functionalized with biochar and CoO nanoparticles exhibits excellent electrochemical performance, including high initial discharge specific capacity of 930.5 mAh g−1 and low capacity decay rate of 0.069 % per cycle during 800 cycles at 1C rate. It is particularly interesting that the CoO nanoparticles distinctively enhance the Li+ diffusion during the charge process, endowing the material with excellent high-rate charging performance. This could be beneficial for the development of Li-S batteries with fast charging feature.

Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features

In order to solve the problem of the traditional iterative closest point algorithm (ICPA), which requires a high initial position of point cloud and improves the speed and accuracy of point cloud registration, a new registration method is proposed in this paper. Firstly, the rough registration method is optimized. As for the extraction of the feature points, a new method of feature point extraction is adopted, which can better keep the features of the original point cloud. At the same time, the traditional point cloud filtering method is improved, and a voxel idea is introduced to filter the point cloud. The edge length data of the voxels is determined by the density, and the experimentally verified noise removal rates for the 3D cloud data are 95.3%, 98.6%, and 93.5%, respectively. Secondly, a precise registration method that combines the curvature feature and fast point feature histogram (FPFH) is proposed in the precise registration stage, and the algorithm is analyzed experimentally. Finally, the two point cloud data sets Stanford bunny and free-form surface are analyzed and verified, and it is concluded that this method can reduce the error by about 40.16% and 36.27%, respectively, and improve the iteration times by about 42.9% and 37.14%, respectively.

Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization

State-of-the-art approaches for localization and mapping are based on local features in images. Along with these features, modern augmented and mixed-reality devices enable building a mesh of the surrounding space. Using this mesh map, we can solve the problem of cross-device localization. This approach is independent of the type of feature descriptors and SLAM used onboard the AR/MR device. The mesh could be reduced to the point cloud that only takes vertices. We analyzed and compared different point cloud registration methods applicable to the problem. In addition, we proposed a new pipeline Feature Inliers Graph Registration Approach (FIGRA) for the co-localization of AR/MR devices using point clouds. The comparative analysis of Go-ICP, Bayesian-ICP, FGR, Teaser++, and FIGRA shows that feature-based methods are more robust and faster than ICP-based methods. Through an in-depth comparison of the feature-based methods with the usual fast point feature histogram and the new weighted height image descriptor, we found that FIGRA has a better performance due to its effective graph-theoretic base. The proposed pipeline allows one to match point clouds in complex real scenarios with low overlap and sparse point density.