Fast Detection Method Research Articles

Real-time motion detection with candidate masks and region growing for moving cameras

A fast and reliable motion detection method is proposed for moving cameras. Existing methods generally build background models and then apply background subtraction for moving pixels detection. In background modeling, statistical methods and/or features, such as edges, local binary patterns are used. Parameters of the proposed methods need to be updated adaptively for better performance. In recent studies, adaptive learning rates are used with main and candidate background models. Simple and adaptive thresholding methods are used for foreground detection in different studies. We build a statistical background model with hue–saturation–value color space and perform background subtraction with the contribution of optical flow vectors. Dense optical flow is applied between warped the background model and the current frame to estimate optical flow vectors. If a pixel has a larger magnitude, we set a weight value for each pixel in the background subtraction process. Then, we extract two candidate foreground masks and apply some postprocessing techniques to determine the final mask. Our proposed method works fast in real time, and the experimental results on two different datasets show that our approach has the highest performance compared with the methods in the literature.

Isolation of small extracellular vesicles from a drop of plasma via EXODUS and their fingerprint proteomics profiling by MALDI-TOF MS

Small extracellular vesicles (sEVs) in blood have emerged as the most promising biomarkers for clinical diagnostics and prognostics. However, isolation and identification of intact sEVs from blood are the major obstacles for basic research and clinical translations. Here, we report rapid isolation and sensitive detection of plasma sEVs by an integrative platform of sEV detection via the ultrafast-isolation system (EXODUS) and the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). We can achieve label-free isolation of sEVs with relatively high recovery and purity by the EXODUS purification method from 20 μL of plasma, which was compared with polyethylene glycol-based precipitation and ultracentrifugation methods. We have profiled the fingerprints of the intact sEVs isolated from the different volumes of plasma using MALDI-TOF MS within 1 h. Further, we have evaluated the reproducibility and identified the metabolomic biomarkers of plasma sEVs via LC-ESI-MS/MS. We believe the combination of rapid EXODUS isolation and MALDI-TOF MS detection may serve as a clinical translation method for fast and high-throughput biomarker detection and screening.

A sandwich-based evanescent wave fluorescent biosensor for simple, real-time exosome detection†

Exosomes are regarded as a promising biomarker for the noninvasive diagnosis and treatment of diseases. The value of exosomes for medical research has promoted the search for a fast, efficient, and sensitive detection method. This study reported a sandwich-based evanescent wave fluorescent biosensor (S-EWFB) for exosome detection. A two-step strategy was implemented to take advantages of the simple binding of fluorescent probes with exosomes via the hydrophobic interaction between the cholesteryl and phospholipid bilayer membrane, as well as real-time detection on an evanescent wave liquid-solid interface based on CD63 aptamer-specific capture to form an exosome@fluorescence probe/aptamer sandwich structure. The one-to-many connection between exosomes and signal molecules and the aptamer-modified evanescent wave optical fiber detection platform reduced the detection limit of exosomes to 7.66 particles/mL, with a linear range of 47.5–4.75 × 106 particles/mL. The entire detection process was simple, rapid, and real-time and lasted about 1 h while requiring no separation and purification. Additionally, this platform showed excellent surface regeneration capability and exhibited good performance during the analysis of tumor and non-tumor-derived exosomes.

SERS and advanced chemometrics – Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment

The enormous development and expansion of antibiotic-resistant bacterial strains impel the intensive search for new methods for fast and reliable detection of antibiotic susceptibility markers. Here, we combined DNA-targeted surface functionalization, surface-enhanced Raman spectroscopy (SERS) measurements, and subsequent spectra processing by decision system (DS) for detection of a specific oligonucleotide (ODN) sequence identical to a fragment of blaNDM-1 gene, responsible for β-lactam antibiotic resistance. The SERS signal was measured on plasmonic gold grating, functionalized with capture ODN, ensuring the binding of corresponded ODNs. Designed DS consists of a Siamese neural network (SNN) coupled with robust statistics and Bayes decision theory. The proposed approach allows manipulation with complex multicomponent samples and predefine the desired detection level of confidence and errors, automatically determining the number of required spectra and samples. In constant to commonly used classification-type SNN, our method was applied to analyze samples with compositions previously “unknown” to DS. The detection of targeted ODN was performed with ≥99% level of confidence up to 3 × 10−12 M limit on the background of 10−10 M concentration of similar but not targeted ODNs.

RNN-based Fault Detection Method for MMC Photovoltaic Gridconnected System

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through Modular Multilevel Converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds, and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.

Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

AbstractComplex networks represent various real‐world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed. Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real‐world known networks, including Zachary, Dolphins, and COVID‐19 Co‐authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state‐of‐the‐art overlapping community detection algorithms in terms of recall, precision, F‐score and overlapping normalized mutual information.

Simultaneous extraction of multi-antibiotic residues in environmental water by DTPA-modified polyaniline nanofibers membrane

In this study, novel diethylenetriaminepentaacetic acidmodified polyaniline nanofibers membrane (DTPA-PANI NFsM) was designed and fabricated. By the synergistic effect of PANI and DTPA, the DTPA-PANI NFsM demonstrated superior sorption performance towards three classes of antibiotics with very different properties: phenicols, tetracyclines and sulfonamides. The mass transfer efficiency of DTPA-PANI NFsM was particularly high. From the isotherm and thermodynamic studies, favorable and spontaneous adsorption processes were confirmed. The potential adsorption mechanisms were investigated by FT-IR analysis. Under the optimized conditions, an efficient, fast, sensitive and economical method for the high-throughput detection of antibiotic residues in environmental water was developed. Low detection limits (1.6–28.6 ng/L for phenicols, 10–34.3 ng/L for tetracyclines, 0.46–15 ng/L for sulfonamides), broad linear range (1.5 ng/L-5 µg/L) as well as satisfactory recoveries and precisions were obtained. In addition, DTPA-PANI NFsM exhibited excellent regeneration and reusability properties, which could be reused for at least 12 times. Also, the practical application feasibility of the method was confirmed by the test of actual samples.

Comparison of three methods for in vivo quantification of glutathione in tissues of hypertensive rats

Glutathione (γ-L-glutamyl-L-cysteinyl-glycine, GSH) is a tripeptide that is part of the antioxidant defense system and contributes to numerous redox-regulatory processes. In vivo, reduced GSH and oxidized glutathione disulfide (GSSG) are present in redox equilibrium and their ratio provides important information on the cellular redox state. Here, we compared three different methods for in vivo quantification of glutathione in tissues of hypertensive rats, an accepted animal model of oxidative stress. In the present study, we used hypertensive rats (infusion of 1 mg/kg/d angiotensin-II for 7 days) to determine the levels of reduced GSH and/or GSH/GSSG ratios in different tissue samples. We used an HPLC-based method with direct electrochemical detection (HPLC/ECD) and compared it with Ellman’s reagent (DTNB) dependent derivatization of reduced GSH to the GS-NTB adduct and free NTB (UV/Vis HPLC) as well as with a commercial GSH/GSSG assay (Oxiselect). Whereas all three methods indicated overall a decreased redox state in hypertensive rats, the assays based on HPLC/ECD and DTNB derivatization provided the most significant differences. We applied a direct, fast and sensitive method for electrochemical GSH detection in tissues from hypertensive animals, and confirmed its reliability for in vivo measurements by head-to-head comparison with two other established assays. The HPLC/ECD but not DTNB and Oxiselect assays yielded quantitative GSH data but all three assays reflected nicely the qualitative redox changes and functional impairment in hypertensive rats. However, especially our GSH/GSSG values are lower than reported by others pointing to problems in the work-up protocol.

Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification.

Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

Fault detection, location, and classification method on compressed air energy storages based inter‐connected micro‐grid clusters using traveling‐waves, current injection method, on‐line wavelet, and mathematical morphology

A new current injection technique and traveling-waves-based fast and accurate method for fault detection, location, and classification on interconnected compressed air energy storages based micro-grid clusters presented in this paper. The compressed air energy storages based micro-grid clusters increase due to some issues such as DC sources and loads expanding, energy-saving, and the power quality developing. It is necessary to recognize the fault type and location to continue service and prevent outages expansion. In this method, a circuit kit joined to the network. Fault detection is done by analyzing the coupled kits currents and the traveling-waves of the fault current and applying them to a mathematical morphology filter, in the Fault time. Determine the fault location and type done by using a mathematical morphology filter, circuit equations, and current calculations. DC pole to ground and pole to pole faults in lines, loads, and resources are considered as MTDC microgrid disturbances. The presented method was tested in a DC interconnected micro-grid clusters connected to several energy storages and renewable resources with many faults. The results represent the validity of the proposed method. The main advantages of the proposed fault location and classification technique are higher speed and accuracy than conventional approaches. This method robustly works to variations in fault resistance, sampling frequency, and operates very fine in high impedance fault conditions.

Early detection and classification of fungal infection in garlic (A. sativum) using electronic nose

Garlic (Allium sativum) is an important crop with numerous benefits and uses. This plant is highly exposed to various pathogenic factors, including fungi. Fungi are the most distinct group responsible for planting diseases the loss involved. In this respect, detection of fungal pathogens infection in the early stages is a major challenge in food security to minimize losses as much as possible. Aroma investigation to detect fungal pathogens infection has been widely welcomed in this regard. In the present study, an electronic nose (E-nose) is utilized as a non-destructive and fast method for early detection of fungal infection on garlic that was synthetically infected with Fusariumoxysporum f. sp. Cepae (FU), Alternariaembellisia (syn. Embellisiaallii) (AL), and Botrytis allii (BO). Statistical analyses including ANOVA, PCA, LDA, SVM, and BPNN were employed to evaluate the aroma profile obtained by the E-nose. According to the obtained results, degradation occurs more quickly in the presence of infection. Due to the different destructive effects of each type of infection, the response changes of each sensor toward the aroma of various infection treatments were not the same. Thus, the E-nose can be used as a practical and beneficial tool to detect fungal infection on garlic in the early stages.

Rapid Detection of Thermal Treatment of Honey by Chemometrics-Assisted FTIR Spectroscopy.

Honey, as a nutritious natural sweetener produced by honeybees, offers a unique biochemical composition with great benefit to human health. Transportation and storage conditions as well as violations of processing can lead to decomposition of vitamins, destruction of the integrity of the antioxidant components and enzymes, and further biochemical changes with impact on nutritional quality. We developed a fast detection method of adulterations or changes of honey caused by thermal exposure, which does not require any sample pretreatment. By Fourier-transform infrared spectroscopy, supported by chemometrics methods, we investigated three types of raw honey before and after heat treatment for varying exposure times at different temperatures. Applying principal component analysis and linear discriminant analysis to the preprocessed spectroscopic data, allowed us to discriminate raw honey from thermally altered ones even at low temperatures of 40 °C with high accuracies ≥90%.

A Fast Bubble Detection Method in Microtubes Based on Pulsed Ultrasound.

In the process of biological microfluidic manipulation, the bubbles generated in the tube will seriously reduce the gauging accuracy. This paper introduces an improving method that can estimate the size of microbubbles in real time. Hence, the measurement data of the liquid volume can be modified according to this method. A microbubble detector based on the pulsed-ultrasound method was studied, including the device structure and the working principle. The assessment formula of the microbubbles in the tube was derived from the simulation results, which adopted the two-phase theory. The digital image processing method was applied to fulfill the microbubble calibration. This detection method was applied to measure the microbubbles in the tube and to modify the flow volume in a timely manner. The results of the experiments showed that this method is effective at improving the microflow gauging accuracy.

RDFNet: A Fast Caries Detection Method Incorporating Transformer Mechanism.

Dental caries is a prevalent disease of the human oral cavity. Given the lack of research on digital images for caries detection, we construct a caries detection dataset based on the caries images annotated by professional dentists and propose RDFNet, a fast caries detection method for the requirement of detecting caries on portable devices. The method incorporates the transformer mechanism in the backbone network for feature extraction, which improves the accuracy of caries detection and uses the FReLU activation function for activating visual-spatial information to improve the speed of caries detection. The experimental results on the image dataset constructed in this study show that the accuracy and speed of the method for caries detection are improved compared with the existing methods, achieving a good balance in accuracy and speed of caries detection, which can be applied to smart portable devices to facilitate human dental health management.

Optical Biosensors for Diagnostics of Infectious Viral Disease: A Recent Update.

The design and development of biosensors, analytical devices used to detect various analytes in different matrices, has emerged. Biosensors indicate a biorecognition element with a physicochemical analyzer or detector, i.e., a transducer. In the present scenario, various types of biosensors have been deployed in healthcare and clinical research, for instance, biosensors for blood glucose monitoring. Pathogenic microbes are contributing mediators of numerous infectious diseases that are becoming extremely serious worldwide. The recent outbreak of COVID-19 is one of the most recent examples of such communal and deadly diseases. In efforts to work towards the efficacious treatment of pathogenic viral contagions, a fast and precise detection method is of the utmost importance in biomedical and healthcare sectors for early diagnostics and timely countermeasures. Among various available sensor systems, optical biosensors offer easy-to-use, fast, portable, handy, multiplexed, direct, real-time, and inexpensive diagnosis with the added advantages of specificity and sensitivity. Many progressive concepts and extremely multidisciplinary approaches, including microelectronics, microelectromechanical systems (MEMSs), nanotechnologies, molecular biology, and biotechnology with chemistry, are used to operate optical biosensors. A portable and handheld optical biosensing device would provide fast and reliable results for the identification and quantitation of pathogenic virus particles in each sample. In the modern day, the integration of intelligent nanomaterials in the developed devices provides much more sensitive and highly advanced sensors that may produce the results in no time and eventually help clinicians and doctors enormously. This review accentuates the existing challenges engaged in converting laboratory research to real-world device applications and optical diagnostics methods for virus infections. The review’s background and progress are expected to be insightful to the researchers in the sensor field and facilitate the design and fabrication of optical sensors for life-threatening viruses with broader applicability to any desired pathogens.

Study on a fast non-contact detection method for key parameters of refractory organic wastewater treatment

In this paper, a fast non-contact detection method is proposed to test the key parameters of refractory organic wastewater in the treatment system intelligently and efficiently. The proposed method combines different preprocessing to the hyperspectral data sampled in the Polyvinyl Alcohol (PVA)-sizing wastewater with our independent-designed equipment and optimizes the models of neural network to predict the pH, COD and PVA of wastewater detection. The data preprocessing contains original spectrum (R), envelope removal (CR), original spectrum continuous projection (R-SPA) and envelope removal continuous projection (CR-SPA). The BP, RBF, SVR and RF networks are trained with the preprocessed hyperspectral data to obtain the optimal inversion prediction models. When the wavelength of the hyperspectral data for discipline ranges from 400 nm to 1700 nm, the trained RF network gets the optimal prediction model of pH after the data preprocessing by CR and its Root Mean Square Error (RMSE) is 0.0424. The RBF network trained by the data pretreated with R-SPA gets the optimal prediction model of COD and its RMSE is 40.4489. The RF network trained with the data preprocessed with the R-SPA gets the optimal PVA index prediction model and its RMSE is 1.5841. The results show that the proposed method can realize the accurate prediction for the pH, COD and PVA values of PVA-sizing wastewater and accomplish it smartly and fast.

Secure IoT edge: Threat situation awareness based on network traffic

Threat situation awareness is one of the new major technologies to avoid network attacks and ensure equipment security. Facing the current IoT network architecture which is characterized by end equipments’ complex services, huge traffic and computing marginalization, real time threat situation awareness based on network traffic can effectively warn and clean latent threat. However, the existing threat situation awareness methods are mostly unitary and dependent on the central node for collection, detection and cleaning. First, it takes too much bandwidth and is not suitable for high-speed scenes. Second, the transmission of traffic or log leads to poor privacy and risk of leakage. Most of all, the perception time is too long, which leads to the performance degradation. This paper proposes a threat situation awareness architecture based on IoT edge and network traffic. Firstly, this paper designs an edge computing device SIE based on CPU and FPGA, the FPGA pipeline is used to analyze the traffic and summarize it in real time. A fast threat situation detection method deployed on SIE’s CPU is proposed which uses flow entropy algorithm to generate situation information. Secondly, this paper introduces the threat situation understanding method based on machine learning. It improves the AdaBoost algorithm and uses uploaded situation information to judge the threat in the traffic. Finally, the method obtains the defensive measure according to the threat intelligence. It can issue the SIE for situation projection and completes threat situation awareness closed loop. Experimental results on KDD99, UNSW-NB15 show that under the premise of ensuring the normal business of IoT equipment and the second level early warning ability, the proposed method can still show good performance under the recognition recall rate, success rate of cleaning threat and other indicators.

Fused-Deep-Features Based Grape Leaf Disease Diagnosis

Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production.

A Fast Intrusion Detection Method for High-Speed Railway Clearance Based on Low-Cost Embedded GPUs.

The efficiency and the effectiveness of railway intrusion detection are crucial to the safety of railway transportation. Most current methods of railway intrusion detection or obstacle detection are inappropriate for large-scale applications due to their high cost or limited coverage. In this study, we present a fast and low-cost solution to intrusion detection of high-speed railways. As the solution to heavy computational burdens in the current convolutional-neural-network-based detection methods, the proposed method is mainly a novel neural network based on the SSD framework, which includes a feature extractor using an improved MobileNet and a lightweight and efficient feature fusion module. In addition, aiming to improve the detection accuracy of small objects, the feature map weights are introduced through convolution operation to fuse features at different scales. TensorRT is employed to optimize and deploy the proposed network in the low-cost embedded GPU platform, NVIDIA Jetson TX2, to enhance the efficiency. The experimental results show that the proposed methods achieved 89% mAP on the railway intrusion detection dataset, and the average processing time for a single frame was 38.6 ms on the Jetson TX2 module, which satisfies the need of real-time processing.

A simple electroanalysis of polyG RNA in mixtures with 3′,5′-cyclic guanosine monophosphate achieved by selective desorption of the monomers from the electrode surface

• A simple label-free technique for analysis of nucleic acid polymerization mixtures. • The technique is based on selective removal of interfering monomers from electrode. • Monomers are removed by treatment with a surfactant or water at elevated temperature. Previously it has been shown that cyclic nucleoside monophosphates can spontaneously polymerize to form RNA oligonucleotides under conditions simulating prebiotic conditions on Archean Earth. The most efficient polymerization was documented with 3′,5′-cyclic guanosine monophosphate (cGMP). In this work a method for fast detection of short polyG RNAs present in a large overabundance of cGMP, modeling conditions in the non-enzymatic nucleotide polymerization mixtures, is presented. The method is based on electrochemical measurements of guanine (G) oxidation signals yielded by RNA oligomers adsorbed onto the surface of a pyrolytic graphite electrode (PGE). To avoid false positive results arising from the G oxidation signals due to co-adsorbed cGMP, a method for selective removal of the monomers from the electrode surface has been devised. In the first step, both cGMP and RNAs are co-adsorbed onto the PGE surface. In the second step, the cGMP is selectively desorbed using treatments in solutions of different tested surfactants (SDS, Tween 20 or Triton X-100), or by washing in deionized water at elevated temperature. We show that this new approach is suitable for selective analysis of products of polymerization reactions from mixtures of their building blocks.