Active Detection Method Research Articles

Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation.

Behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in Tourette syndrome. Detecting tic symptoms plays an important role in patient treatment and evaluation; accurate tic identification is the key to clinical diagnosis and evaluation. In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how the patient's twitches change over time. The detection results at the individual level indicated that our method can effectively detect tic actions in videos of Tourette patients without the need for fine labeling, which is significant for the long-term evaluation of patients with Tourette syndrome.

A Framework for Personalized Human Activity Recognition

In today’s world, Human Activity Recognition (HAR) through video streams is actively used in every aspect of our life, such as automated surveillance systems and sports statistics are computed according to the videos with the help of HAR. Activity detection is not a new subject, and several methods are available. However, the most recent and most promising techniques rely on Convolutional Neural Networks (CNNs). CNNs primary usage is based on a single image frame to perform logical or categorical identification of an object, scene, or activity. We exploit this feature to adapt CNN on video streams to achieve HAR. In this study, we present a Personalized HAR (PHAR) framework that increases activity recognition accuracy with Object Detection (OD). First, we demonstrate the state-of-the-art HAR and OD methods in the literature. Then we illustrate our framework with two new Single Person Human Activity Recognition models. Finally, the performance of the new framework is evaluated with the well-known activity detection methods. Results show that our new PHAR model with 95% accuracy ratio outperforms the CNN-LSTM-based reference model (90%). Moreover, a new metric Average Accuracy Score (AAS) is described in this study, PHAR models approximately have 94% AAS, which is better than the reference model with 89% AAS.

Probing the molecular mechanism of interaction between polystyrene nanoplastics and catalase by multispectroscopic techniques

Nanoplastics are emerging pollutants that pose a potential threat to the environment and organisms and are widely distributed in environmental samples and food chains. The accumulation of polystyrene nanoplastics (PS-NPs) in an organism can cause oxidative stress. Currently, toxicity studies of PS-NPs mainly focus on the individual and cellular levels, whereas few studies have been conducted on the molecular mechanisms of the interaction between PS-NPs and catalase (CAT). Based on this, CAT was chosen as the target receptor for molecular toxicity research to reveal the interaction mechanism at the molecular level between PS-NPs and CAT by using various spectroscopic means and enzyme activity detection methods. The results indicated that PS-NPs destroyed the secondary structure of CAT, causing its protein skeleton to loosen and unfold, increasing the content of α-helices, decreasing the content of β-sheets, and exposing the position of the heme group. After exposure to PS-NPs, the internal fluorophore of CAT underwent fluorescence sensitization, resulting in a micelle-like structure, which enhanced the hydrophobicity of aromatic amino acids but did not change their polarity. In addition, the aggregation state of CAT was altered upon binding to PS-NPs, and the volume was further increased. Finally, these structural changes led to a gradual decrease in CAT activity. This study presents a comprehensive assessment of the toxicity of PS-NPs at the molecular level, which can provide more experimental support for the study of the biotoxicological efficacy of PS-NPs.

Multi-scale multivariate detection method for the effective impact range of hydraulic fracturing in coal seam

Hydraulic fracturing enhances coal seam permeability and reduces original gas content. Determining the effective range of hydraulic fracturing is essential in evaluating its effectiveness. This paper analyzes physical field changes during hydraulic fracturing and proposes a multi-scale multivariate detection method for determining the effective range of hydraulic fracturing in coal seams based on changes in wave velocity and resistivity due to fracture expansion.The method was validated through field experiments. The result shows that vibration wave computed tomography (VWCT) is a passive, large-scale area detection method. In contrast, the direct current (DC) method is a small-scale, local active detection method. In the hydraulic fracturing process, the microseismic (MS) events increase. After fracturing, the wave velocity of coal seam decreases significantly, indicating the formation of fractures around the fractured holes and a fracture zone. The fracturing expands the pre-existing fractures adjacent to fractured holes, forming a fracture network between them and natural fractures, leading to a decrease in wave velocity around the coal seam. The DC detection results indicate a noticeable development of pores around the fractured holes following hydraulic fracturing. The increased resistance value area is mainly concentrated on both sides of the 9–4 drilling, with an influence range of approximately 30 m, and an increased range of 100–400 Ω·m, indicating a hydraulic fracturing pore expansion range of 30 m in coal seams. Comprehensively, the maximum impact area of fracturing is the area surrounding the fractured holes, and the impact area is not uniform. The DC method test results reflect the extension of fracturing around fractured holes, while the VWCT test results represent the extension of fracturing around fractured holes and the connectivity of primary fractures, both of which include each other.

An Enhanced Algorithm for Active Fire Detection in Croplands Using Landsat-8 OLI Data

Burning biomass exacerbates or directly causes severe air pollution. The traditional active fire detection (AFD) methods are limited by the thresholds of the algorithms and the spatial resolution of remote sensing images, which misclassify some small-scale fires. AFD for burning straw is interfered with by highly reflective buildings around urban and rural areas, resulting in high commission error (CE). To solve these problems, we developed a multicriteria threshold AFD for burning straw (SAFD) based on Landsat-8 imagery in the context of croplands. In solving the problem of the high CE of highly reflective buildings around urban and rural areas, the SAFD algorithm, which was based on the LightGBM machine learning method (SAFD-LightGBM), was proposed to differentiate active fires from highly reflective buildings with a sample dataset of buildings and active fires and an optimal feature combining spectral features and texture features using the ReliefF feature selection method. The results revealed that the SAFD-LightGBM method performed better than the traditional threshold method, with CE and omission error (OE) of 13.2% and 11.5%, respectively. The proposed method could effectively reduce the interference of highly reflective buildings for active fire detection, and it has general applicability and stability for detecting discrete, small-scale fires in urban and rural areas.

Statistical Device Activity Detection for OFDM-Based Massive Grant-Free Access

Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of things (IoT), mainly concentrate on narrow band systems under flat fading. In contrast, this paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme. Then, we propose two different but equivalent models for the received pilot signal. Specifically, one directly models the received signal for actual devices, whereas the other can be interpreted as a signal model for virtual devices. The two signal models are insightful and essential for designing various device activity detection and channel estimation methods for OFDM-based massive grant-free access. Next, we systematically investigate statistical device activity detection under frequency-selective Rayleigh fading based on the two signal models. In particular, in the case without prior knowledge of device activities, we model device activities as deterministic but unknown binary constants and propose three maximum likelihood (ML) estimation-based device activity detection methods with different detection accuracies and computation times. In the case with prior knowledge of device activities, we model device activities as realizations of Bernoulli random variables with a known joint distribution, which appropriately incorporates the prior knowledge, and propose three maximum a posterior probability (MAP) estimation-based device activity methods, which further enhance the accuracies of the corresponding ML estimation-based methods at the cost of increased computational complexities. The proposed methods can meet diverse practical needs for OFDM-based massive grant-free access.

SEMtree: tree-based structure learning methods with structural equation models.

With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework. Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance. SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

A Review of Video Action Detection Based on Deep Learning

Currently, the application of deep learning to solving problems associated with traditional surveillance video analysis has become one of the research hot topics. The video action detection is referred to as detecting the temporal segments containing the action in the video as temporal action proposals. The existing work is mainly classified into two categories: one is to use the low-level details of video to generate action proposals; the other is to use the high-level semantics of video to generate action proposals. By deeply researching the video action detection methods based on deep learning, this paper is an attempt to find out problems with the existing methods and put forward some suggestion for improvement.

An ultra-sensitive and specific UCBiosensor via CRISPR-Cas12a and UDG-mediated polymerase chain reaction

DNA damage is an important cause of several diseases. Uracil DNA glycosylase (UDG) is a key enzyme in DNA damage repair and used as an early diagnostic marker and a prognostic indicator for cancer. Therefore, there is an urgent need to develop a simple, low-background, ultra-sensitive UDG activity detection method for clinical applications. At present, biosensors based on CRISPR-Cas12a have become a powerful diagnostic tool owing to their high sensitivity. Here, we have established an ultra-sensitive biosensor for detecting UDG activity based on CRISPR-Cas12a coupled UDG-mediated polymerase chain reaction (U-PCR), which has the advantages of simplicity, low background, ultra-sensitivity, and specificity. We used this biosensor to multiply amplify the UDG signal, and the detection limit reached 3.5 × 10−6 U/mL, which is one of the most sensitive methods for UDG activity detection. Finally, we detected UDG activity in the lysates of cancer cells, indicating that this method could be used to detect UDG activity in real samples. The UDG inhibition test showed that the method we established is a useful tool for screening UDG inhibitors. We believe that the UDG activity detection biosensor established in this study has potential applications in biomedical research and clinical diagnosis.

Efficient Pause Extraction and Encode Strategy for Alzheimer's Disease Detection Using Only Acoustic Features from Spontaneous Speech.

Clinical studies have shown that speech pauses can reflect the cognitive function differences between Alzheimer's Disease (AD) and non-AD patients, while the value of pause information in AD detection has not been fully explored. Herein, we propose a speech pause feature extraction and encoding strategy for only acoustic-signal-based AD detection. First, a voice activity detection (VAD) method was constructed to detect pause/non-pause feature and encode it to binary pause sequences that are easier to calculate. Then, an ensemble machine-learning-based approach was proposed for the classification of AD from the participants' spontaneous speech, based on the VAD Pause feature sequence and common acoustic feature sets (ComParE and eGeMAPS). The proposed pause feature sequence was verified in five machine-learning models. The validation data included two public challenge datasets (ADReSS and ADReSSo, English voice) and a local dataset (10 audio recordings containing five patients and five controls, Chinese voice). Results showed that the VAD Pause feature was more effective than common feature sets (ComParE: 6373 features and eGeMAPS: 88 features) for AD classification, and that the ensemble method improved the accuracy by more than 5% compared to several baseline methods (8% on the ADReSS dataset; 5.9% on the ADReSSo dataset). Moreover, the pause-sequence-based AD detection method could achieve 80% accuracy on the local dataset. Our study further demonstrated the potential of pause information in speech-based AD detection, and also contributed to a more accessible and general pause feature extraction and encoding method for AD detection.

Conceptual Model, Experiment and Numerical Simulation of Diaphragm Wall Leakage Detection Using Distributed Optical Fiber.

Leakage in the diaphragm wall is difficult to detect in deep foundation pits. In this study, the conceptual model of active and passive thermal leak detection methods was proposed according to the occurrence of temperature field anomalies caused by seepage. Experiments were performed using a heating system and an optical fiber temperature measurement system to verify the thermal leakage detection systems. Numerical simulations were performed to understand the mechanism of the detecting method. Results indicated that the optical cable could detect the low-temperature anomaly in the active temperature field leak detection. The arrangement method of the leakage detection system was also presented in actual engineering.

Method for Detecting Electrical Interface in Sea Area Based on Active Electric Field

To obtain the direction and distance of the electrical interface in the sea, the active electric field of the electric dipole and two three-component electric field intensity sensors are used to detect them. First, the structure design of the active electric field detection device is given, and then a method for active electric field detection of the electrical interface in the sea area is proposed, that is, the effect of the electrical interface on the active electric field is equivalent to the effect of the mirror electric dipole, locating the position of the mirror dipole can be done by measuring the change of the field strength, and then the interface parameters can be determined according to the symmetrical relationship between the position of the mirror dipole and the source dipole. Both simulation analysis and laboratory test results show that the interface detection method is feasible. The related research provides new ideas for target detection in the sea.

An Active Image Forgery Detection Approach Based on Edge Detection

Recently, digital images have become the most used data, thanks to high internet speed and high resolution, cheap and easily accessible digital cameras. We generate, transmit and store millions of images every second. Most of these images are insignificant images containing only personal information. However, in many fields such as banking, finance, public institutions, and educational institutions, the images of many valuable objects like ID cards, photographs, credit cards, and transaction receipts are stored and transmitted to the digital environment. These images are very significant and must be secured. A valuable image can be maliciously modified by an attacker. The modification of an image is sometimes imperceptible even by the person who stored the image. In this paper, an active image forgery detection method that encodes and decodes image edge information is proposed. The proposed method is implemented by designing an interface and applied on a test image which is frequently used in the literature. Various tampering attacks are simulated to test the fidelity of the method. The method not only notifies whether the image is forged or not but also marks the tampered region of the image. Also, the proposed method successfully detected tampered regions after geometric attacks, even on self-copy attacks. Also, it didn’t fail on JPEG compression.

Graph-Based Active Learning With Uncertainty and Representativeness for Industrial Anomaly Detection

Anomaly detection is essential for automated industrial production. Sufficiently labeled samples play an important role in improving the detection capability of the model. However, existing anomaly detection methods cannot balance the detection performance against the labeling cost in practice owing to the lack of an effective and efficient sample selection strategy. To address this issue, a graph-based active anomaly detection method called GAAD is proposed in this article. In the proposed method, a graph structure is adopted to rapidly spread the labeling information, and a heuristic strategy is designed to select samples combined with uncertainty and representativeness, which can rapidly and adequately explore the sample distribution with limited labeled samples. Moreover, a simple but effective autoencoder is proposed to confuse low-level features and preserve the locality by a pre-trained model, which achieves a better image encoding and anomaly detection performance. We created a new through-hole technology solder joint dataset (THT) and conducted extensive comparative experiments with mainstream active and semi-supervised anomaly detection methods on both THT and the publicly available NEU datasets. In the experiments, GAAD achieved the best detection performance and had an extremely low update time of 0.0071 s during active learning, demonstrating its superiority for industrial applications.

Study on Active Sleeping Node Detection Method in Sensor Network Based on Multi-dimensional Sliding Window

Study on Active Sleeping Node Detection Method in Sensor Network Based on Multi-dimensional Sliding Window

DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos.

Video anomaly detection (VAD) has been extensively studied for static cameras but is much more challenging in egocentric driving videos where the scenes are extremely dynamic. This paper proposes an unsupervised method for traffic VAD based on future object localization. The idea is to predict future locations of traffic participants over a short horizon, and then monitor the accuracy and consistency of these predictions as evidence of an anomaly. Inconsistent predictions tend to indicate an anomaly has occurred or is about to occur. To evaluate our method, we introduce a new large-scale benchmark dataset called Detection of Traffic Anomaly (DoTA)containing 4,677 videos with temporal, spatial, and categorical annotations. We also propose a new VAD evaluation metric, called spatial-temporal area under curve (STAUC), and show that it captures how well a model detects both temporal and spatial locations of anomalies unlike existing metrics that focus only on temporal localization. Experimental results show our method outperforms state-of-the-art methods on DoTA in terms of both metrics. We offer rich categorical annotations in DoTA to benchmark video action detection and online action detection methods. The DoTA dataset has been made available at: https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly.

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

<abstract> <p>The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/zhaoyw456/DIPRAS">https://github.com/zhaoyw456/DIPRAS</ext-link>.</p> </abstract>

Voice activity detection for audio signal of voyage data recorder using residue network and attention mechanism

ABSTRACT In this paper, a voice activity detection (VAD) method based on residue network and attention mechanism for VDR audio signal is proposed. First, several frame-wise acoustic features are extracted to eliminate audio data amount. Then, the frame-wise features are reshaped to construct 2-D feature maps. To facilitate parameter optimisation, a residue network is utilised herein to learn the complex mapping function from acoustic features to VAD output. Furthermore, to focus more on local information and suppress useless data, an attention mechanism is combined with the residue network to extract complex hidden information from the 2-D feature maps. Finally, the compressed 2-D features are flattened and refined by a dense layer to cover global information. The proposed method can automatically learn the mapping function efficiently and effectively. Experimental results show that the proposed method achieves the highest AUC, and the second highest ACC and F-measure compared with reference methods on the annotated real-world VDR audio dataset.

A targeted search for Main Belt Comets

ABSTRACT Main Belt Comets (MBCs) exhibit sublimation-driven activity while occupying asteroid-like orbits in the Main Asteroid Belt. MBCs and candidates show stronger clustering of their longitudes of perihelion around 15° than other objects from the Outer Main Belt (OMB). This potential property of MBCs could facilitate the discovery of new candidates by observing objects in similar orbits. We acquired deep r-band images of 534 targeted asteroids using the Isaac Newton Telescope/Wide Field Camera between 2018 and 2020. Our sample is comprised of OMB objects observed near perihelion, with longitudes of perihelion between 0 and 30° and orbital parameters similar to known MBCs. Our pipeline applied activity detection methods to 319 of these objects to look for tails or comae, and we visually inspected the remaining asteroids. Our activity detection pipeline highlighted a faint antisolar tail-like feature around 2001 NL19 (279870) observed on 2018 November 07, 6 months after perihelion. This is consistent with cometary activity; however, additional observations of this object will be needed during its next perihelion to investigate its potential MBC status. If it is active our survey yields a detection rate of ∼1:300, which is higher than previous similar surveys, supporting the idea of dynamical clustering of MBCs. If not, it is consistent with previously estimated abundance rates of MBCs in the OMB (<1:500).

FarNet-II: An improved solar far-side active region detection method

Context. Activity on the far side of the Sun is routinely studied through the analysis of the seismic oscillations detected on the near side using helioseismic techniques such as phase-shift sensitive holography. Detections made through those methods are limited to strong active regions due to the need for a high signal-to-noise ratio. Recently, the neural network FarNet was developed to improve these detections. This network extracts more information from helioseismic far-side maps, enabling the detection of smaller and weaker active regions. Aims. We aim to create a new machine learning tool, FarNet-II, which further increases FarNet’s scope, and to evaluate its performance in comparison to FarNet and the standard helioseismic method for detecting far-side activity. Methods. We developed FarNet-II, a neural network that retains some of the general characteristics of FarNet but improves the detections in general, as well as the temporal coherence among successive predictions. The main novelties of the new neural network are the implementation of attention and convolutional long short-term memory (ConvLSTM) modules. A cross-validation approach, training the network 37 times with a different validation set for each run, was employed to leverage the limited amount of data available. We evaluate the performance of FarNet-II using three years of extreme ultraviolet observations of the far side of the Sun acquired with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of activity. The results from FarNet-II were compared with those obtained from FarNet and the standard helioseismic method using the Dice coefficient as a metric. Given that the application of the ConvLSTM modules can affect the accuracy as a function of the position on the sequence, we take this potential dependency into account in the evaluation. Results. FarNet-II achieves a Dice coefficient that improves that of FarNet by over 0.2 points for every output position on the sequences from the evaluation dates. Its improvement over FarNet is higher than that of FarNet over the standard method. Conclusions. The new network is a very promising tool for improving the detection of activity on the far side of the Sun given by pure helioseismic techniques. Space weather forecasts can potentially benefit from the higher sensitivity provided by this novel method.