Automatic Labeling Research Articles

Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications

We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural network for object recognition from radar data. The automatic labeling approach rests on the simultaneous recording of camera and lidar data in addition to the radar spectrum. By warping radar spectra into the camera image, state-of-the-art object recognition algorithms can be applied to label relevant objects, such as cars, in the camera image. The warping operation is designed to be fully differentiable, which allows backpropagating the gradient computed on the camera image through the warping operation to the neural network operating on the radar data. As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors. The proposed scene flow estimation approach is compared against a state-of-the-art scene flow algorithm, and it outperforms it by approximately 30% w.r.t. mean average error. The feasibility of the overall framework for automatic label generation for RD spectra is verified by evaluating the performance of neural networks trained with the proposed framework for Direction-of-Arrival estimation.

Road scene classification based on street-level images and spatial data

Understanding the context of the scene is one of the most important aspects for new generation of autonomous vehicles. It is a very trivial task for a human to recognize the scene context by only a single look at the picture, however, for a computer, it is still a challenging task. This problem can be solved by automatic data labeling using deep-learning models for scene classification. Relying on scene type labels we can select relevant scenes to prepare a balanced dataset to train more advanced instance detection models using data from a specific road condition. This study presents a novel framework based on a deep convolutional neural network (CNN) for the automatic road scene classification of street-level automotive images. For the evaluation of our approach, we use a well-known autonomous benchmark dataset, from which we extract geo-position data and combine them with predictions from the scene classification model to get ground truth labels to train and evaluate a ResNet-50 model for scene classification. The results and comparison with state-of-the-art methods are presented.

A novel IPTV framework for automatic TV commercials detection, labeling, recognition and replacement

A novel IPTV framework for automatic TV commercials detection, labeling, recognition and replacement

Lumen: A machine learning framework to expose influence cues in texts

Phishing and disinformation are popular social engineering attacks with attackers invariably applying influence cues in texts to make them more appealing to users. We introduce Lumen, a learning-based framework that exposes influence cues in text: (i) persuasion, (ii) framing, (iii) emotion, (iv) objectivity/subjectivity, (v) guilt/blame, and (vi) use of emphasis. Lumen was trained with a newly developed dataset of 3K texts comprised of disinformation, phishing, hyperpartisan news, and mainstream news. Evaluation of Lumen in comparison to other learning models showed that Lumen and LSTM presented the best F1-micro score, but Lumen yielded better interpretability. Our results highlight the promise of ML to expose influence cues in text, toward the goal of application in automatic labeling tools to improve the accuracy of human-based detection and reduce the likelihood of users falling for deceptive online content.

CAVE-SC: Inferring categories for venues using check-ins

The categories of venues are labels, such as Museum and Bar, assigned to geographic locations. They are often incomplete and new venues may constantly arise, making automatic venue labeling essential. In this study, we proposed a category-aware venue embedding model with similarity constraints (CAVE-SC), which is an automatic feature engineering method to infer category labels for venues. CAVE-SC first models the co-occurrence relations between a given venue and its two types of sequential contexts (namely, the venue context and the category context) based on a softmax loss and then projects both venues and categories into the same latent space. Furthermore, CAVE-SC models the similarity constraints posed by the relationships between venues and their categories based on a newly designed triplet loss, assuming that the distance between a venue and its category is smaller than the distance between the venue and a randomly sampled category in the embedding space. Finally, linear interpolation is used to balance the two losses, thereby generating discriminative feature representations of venues. Venue labeling can be performed based on the distances between venue representations and category representations. Extensive experiments on two check-in datasets demonstrate the superiority of CAVE-SC over the baseline methods.

Technology and Application of Digital Nondestructive Prescreening Based on Automated Storage

This project around “DR digital imaging non-destructive prediction based on automatic warehousing screening technology and application” project can be divided into cable full inspection in coil based on DR digital imaging mode of the whole case study, with a small piece of material screening technology research, based on the data acquisition and recognition algorithm used for the detection of automated storage research and pilot test three corpus. The hardware framework structure and software system security access model are designed based on existing warehouse conditions. A non-destructive batch automatic pre-screening equipment is developed for small materials, which has both DR and CT functions. Cold cathode pulse ray source for needle tip pulse discharge is improved. A set of non-destructive pre-screening equipment for the whole length of the cable is developed and arranged on the warehouse automatic tray wire rack. Through the multiangle scan imaging of two ray sources, the application of image recognition is expanded, and the automatic size labeling problem warning is realized. The non-destructive pre-screening of the whole length of the cable is implemented synchronously during the process of unloading the cable from the warehouse. The insulation layer scars of the wire diameter and thickness are compared and checked, and the quality problems are visually reflected, and the detection report is automatically generated.

Discovering a Failure Taxonomy for Early Design of Complex Engineered Systems Using Natural Language Processing

Abstract Methodologies for failure assessment frequently rely on historical failure modes, causes, and recommendations for prevention. Meanwhile, there are growing databases of narrative-based lessons that are under-utilized due to their size. Advances in natural language processing (NLP) enable unsupervised extraction of this knowledge. We present a methodology for (1) identifying relevant information using a term frequency inverse document frequency (TF-IDF) classifier and (2) extracting knowledge for failure assessment using a hierarchical topic modeling approach, hierarchical latent Dirichlet allocation (LDA). To interpret the extracted topics, we apply an automatic topic labeling technique using pointwise mutual information (PMI) extraction. The methodology is applied to NASA’s Lessons Learned Information System (LLIS), which is publicly available. Partitioned topics enable the extraction of three aspects: cause, failure, and recommendation, while a hierarchy enables organization into a taxonomy. The methodology is generalizable to databases containing narrative-style documents, while the results from the LLIS represent a summary of themes in the dataset, expressed in a format that can be linked to early design failure analyses.

Selecting a set of semantic labels to eliminate ambiguity for Vietnamese

The rapid development of automatic control systems in natural language, automatic translation systems based on semantic statistics have been receiving much attention from computer science researchers. However, this method requires a large bilingual corpus and accurate semantic tagging, the construction of which requires a lot of time and effort, because of the ambiguity of the natural language. For Vietnamese, automatic question-and-answer systems are increasingly developing in Vietnam, but the problem of semantic ambiguity has not yet received much attention from domestic studies. In this paper, we build a model to evaluate and select an effective and reasonable set of semantic labels from 3 commonly used sets of semantic labels: LLOCE (Longman Lexicon of Contemporary English), LDOCE (Longman Dictionary) of Contemporary English) and WordNet. And then, select the appropriate set of labels, apply it to automatic semantic labeling systems for Vietnamese, help eliminate semantic ambiguity, and support automatic translation, automatic question-and-answer systems efficiently.

Semantic Segmentation of Agricultural Images Based on Style Transfer Using Conditional and Unconditional Generative Adversarial Networks

Classification, segmentation, and recognition techniques based on deep-learning algorithms are used for smart farming. It is an important and challenging task to reduce the time, burden, and cost of annotation procedures for collected datasets from fields and crops that are changing in a wide variety of ways according to growing, weather patterns, and seasons. This study was conducted to generate crop image datasets for semantic segmentation based on an image style transfer using generative adversarial networks (GANs). To assess data-augmented performance and calculation burdens, our proposed framework comprises contrastive unpaired translation (CUT) for a conditional GAN, pix2pixHD for an unconditional GAN, and DeepLabV3+ for semantic segmentation. Using these networks, the proposed framework provides not only image generation for data augmentation, but also automatic labeling based on distinctive feature learning among domains. The Fréchet inception distance (FID) and mean intersection over union (mIoU) were used, respectively, as evaluation metrics for GANs and semantic segmentation. We used a public benchmark dataset and two original benchmark datasets to evaluate our framework of four image-augmentation types compared with the baseline without using GANs. The experimentally obtained results showed the efficacy of using augmented images, which we evaluated using FID and mIoU. The mIoU scores for the public benchmark dataset improved by 0.03 for the training subset, while remaining similar on the test subset. For the first original benchmark dataset, the mIoU scores improved by 0.01 for the test subset, while they dropped by 0.03 for the training subset. Finally, the mIoU scores for the second original benchmark dataset improved by 0.18 for the training subset and 0.03 for the test subset.

Application of multiple post labeling delay time arterial spin labeling imaging in the quantitative blood flow analysis of brain subregions in healthy adults

Objective: To explore the normal ranges of perfusion parameters between cerebral hemisphere, cerebellar hemisphere and brain anatomical subregions (56 pairs) in different gender and age groups with multiple post labeling delay time (Multi-PLD) arterial spin labeling (ASL) imaging. Methods: From November 2020 to December 2020, 42 healthy adult volunteers (Male 25, Female 17) were recruited to perform 7 PLD ASL imaging, including 21 young adults (15 males and 6 females, aged 23-35 years) and 21 seniors (10 males and 11 females, aged 36-74 years). The data was processed offline by Cereflow software to obtain arterial arrival time (ATT) and corrected cerebral blood flow (CBF) and cerebral blood volume (CBV) perfusion parameters. SimpleITK standardization function was used to standardize the calculated perfusion image according to the anatomical automatic labeling (AAL) template. Therefore, CBF, ATT, CBV perfusion values of brain subregions were obtained. Paired samples t test, Wilcoxon rank sum test, independent samples t test and Mann-Whitney U test were used to compare the differences of perfusion parameters in the cerebral hemisphere, the cerebellar hemisphere, brain subregions depending on side, gender and age. Pearson correlation analysis was used to compare the correlations of perfusion parameters with age. Results: CBF in 62.5% (35/56) subregions and CBV in 44.6% (25/56) subregions were higher in right side than those in left side. ATT in most brain anatomical subregions (16/56) were higher in left side. The CBF [(35.30±8.31) vs. (34.34±7.53) ml·100g-1·min-1, P=0.021], CBV [(0.47±0.11) vs. (0.45±0.09) ml/100g, P<0.001], ATT [(1.30±0.10) vs. (1.24±0.11) s, P<0.001] in left cerebellar hemisphere were higher than that of right side. The CBF (28/56) of cerebral hemisphere, cerebellar hemisphere and brain subregions was higher in females than that in males, while ATT in 83.9% (47/56) subregions was lower than that in males (all P<0.05). CBV in female subjects was higher only in 5 brain regions (superior occipital gyrus, middle occipital gyrus, inferior occipital gyrus, superior parietal gyrus and cerebelum_7b) (all P<0.05). In young subjects, CBF in 44.6% (25/56) subregions and CBV in 33.9% (19/56) subregions were higher than those in the senior group (all P<0.05). The ATT in most subregions in young group were lower than those in senior group, but the difference was statistically significant only in rectus gyrus (P=0.026) and paracentral lobule (P=0.006). The CBF (r=-0.430, P=0.005) and CBV (r=-0.327, P=0.035) of cerebral hemisphere were negatively correlated with age. The CBF (24/25, r range:-0.497 --0.343, all P<0.05) and CBV (16/19, r range:-0.474 --0.322, all P<0.05) in most subregions were negatively correlated with age, while ATT was positively correlated (gyrus rectus: r=0.311, P=0.045; paracentral lobule: r=0.392, P=0.010). Conclusions: Multi-PLD ASL imaging could be applied for quantitative analysis of brain perfusion. The perfusion parameters of anatomical subregions are different depending on side, gender, and age.

Cerebellum regulating cerebral functional cortex through multiple pathways in complete thoracolumbar spinal cord injury.

The previous studies have found significant brain structural and functional changes in cerebral regions after spinal cord injury (SCI), but few studies have explored the cerebellar–cerebral circuit changes in SCI. This study aims to study the brain structural changes of cerebellar subregions and its functional connectivity (FC) changes with cerebrum in complete thoracolumbar SCI (CTSCI), and screen out the regions that play relatively important roles in affecting sensorimotor function. Eighteen CTSCI patients and 18 age- and gender-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) was used to characterize the brain structural changes of cerebellar subregions [from the Anatomical Automatic Labeling (AAL116)], seed-based FC was used to evaluate the cerebellar–cerebral FC changes and support vector machine (SVM) analysis was used to search for sensitive imaging indicators. CTSCI patients showed slightly structural atrophy in vermis_3 (p = 0.046) and significantly decreased FC between cerebellum and cerebral sensorimotor-, visual-, cognitive-, and auditory-related regions (cluster-level FWE correction with p < 0.05). Additionally, SVM weight analysis showed that FC values between vermis_10 and right fusiform gyrus had the greatest weight in functional changes of CTSCI. In conclusion, different degrees of structural and functional changes occurred in each subregion of cerebellum following CTSCI, and FC change between vermis_10 and right fusiform gyrus plays the most important role in dysfunction and may become an important neural network index of rehabilitation therapy.

Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data via hierarchical clustering analysis.

ObjectiveWe wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes.MethodsEighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups.ResultsTwo subtypes of PD were identified. The “diffuse malignant subtype” was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The “mild subtype” was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function.ConclusionHierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.

Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling

The construction industry has made great strides in recent decades by utilizing computer programs, including computer aided design programs. However, compared to the manufacturing sector, labor productivity is low because of the high proportion of knowledge-based tasks and simple repetitive tasks. Therefore, knowledge-based task efficiency should be improved through the visual recognition of information by computers. A computer requires a large amount of training data, such as the ImageNet project, to recognize visual information. This paper proposes façade datasets that are efficiently constructed by quickly collecting façade data through road-view images generated from web portals and automatically labeled using deep learning as part of the construction of image datasets for visual recognition construction by a computer. Therefore, we attempted to automatically label façade images to quickly generate large-scale façade datasets with much less effort than the existing research methods. Simultaneously, we constructed datasets for a part of Dongseong-ro, Daegu Metropolitan City, and analyzed their utility and reliability. It was confirmed that the computer could extract significant façade information from the road-view images by recognizing the visual information of the façade image. In addition, we verified the characteristics of the building construction image datasets. This study suggests the possibility of securing quantitative and qualitative façade design knowledge by extracting façade design information from façades anywhere in the world. Previous studies mainly collected façade images through camera photography to construct databases, but in this study, a significant part of the database construction process was shortened through automation. In the case of façade automatic image labeling studies, it is the façade-based automatic 3D modeling which has been primarily studied, but it is difficult to find a study to extract data for façade design research.

Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study.

IntroductionType 2 diabetes mellitus (T2DM) has been found to be associated with abnormalities of the central and peripheral vascular nervous system, which were considered to be involved in the development of cognitive impairments and erectile dysfunction (ED). In addition, altered brain function and structure were identified in patients with ED, especially psychological ED (pED). However, the similarities and the differences of the central neural mechanisms underlying pED and T2DM with ED (DM-ED) remained unclear.MethodsDiffusion tensor imaging data were acquired from 30 T2DM, 32 ED, and 31 DM-ED patients and 47 healthy controls (HCs). Then, whole-brain structural networks were constructed, which were mapped by connectivity matrices (90 × 90) representing the white matter between 90 brain regions parcellated by the anatomical automatic labeling template. Finally, the method of network-based statistic (NBS) was applied to assess the group differences of the structural connectivity.ResultsOur NBS analysis demonstrated three subnetworks with reduced structural connectivity in DM, pED, and DM-ED patients when compared to HCs, which were predominantly located in the prefrontal and subcortical areas. Compared with DM patients, DM-ED patients had an impaired subnetwork with increased structural connectivity, which were primarily located in the parietal regions. Compared with pED patients, an altered subnetwork with increased structural connectivity was identified in DM-ED patients, which were mainly located in the prefrontal and cingulate areas.ConclusionThese findings highlighted that the reduced structural connections in the prefrontal and subcortical areas were similar mechanisms to those associated with pED and DM-ED. However, different connectivity patterns were found between pED and DM-ED, and the increased connectivity in the frontal–parietal network might be due to the compensation mechanisms that were devoted to improving erectile function.

Analisis Sentiment Pelanggan Terhadap Penilaian Produk Pada Toko Online Shop Amreta Menggunakan Metode Naïve Bayes Classification

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application, namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersis an amreta online shop store , based on the results of the identification of the problem, it was found that the fact was that many comments did not match the stars given so it can be said that the rating cannot represent that the store's performance is good or not. Therefore, to increase the profit of shop work, the amreta still needs to evaluate the store. In conducting an evaluation, the store needs to classify positive, negative or neutral comments. Analysis of customer sentiment towards product assessments in amreta online shop stores using the naive bayes classification method. The use of test data in this study was obtained from the sentiment of amreta online shop consumers as much as 2014 data,then the data was processed through the data cleaning process resulting in net data of 1899 data. Furthermore, the data preprocessing process is divided into 3 stages, namely Tokenize Data, Transform case and Stopword removal. After that, the analysis of data for the automatic labeling stage using Text Vectorize from the process obtained data division into 3 data groups of 71% or 1343 positive data, 3% or 52 negative data and 26% or 504 neutral data. furthermore, it is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model through automatic calculations. The results of the study can be concluded that the test data obtained have an accuracy level of 97.16% using the Naive Bayes Classification model.

Sensor-Based Hand Gesture Detection and Recognition by Key Intervals

This study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in the sequence is regarded as an object with a pair of key intervals. The detection and classification of each gesture are equivalent to the identification and matching of the corresponding key intervals. A simple automatic labelling is proposed for the identification of key intervals without manual inspection of sensory data. This could facilitate the collection and annotation of training data. To attain superior generalization and regularization, a multitask learning algorithm for the simultaneous training for gesture detection and classification is proposed. A prototype system based on smart phones for remote control of home appliances was implemented for the performance evaluation. Experimental results reveal that the proposed algorithm provides an effective alternative for applications where accurate detection and classification of hand gestures by simple networks are desired.

A self-training teacher-student model with an automatic label grader for abdominal skeletal muscle segmentation

Deep learning on a limited number of labels/annotations is a challenging task for medical imaging analysis. In this paper, we propose a novel self-training segmentation pipeline (Self-Seg in short) for segmenting skeletal muscle in CT images. Self-Seg starts with a small set of annotated images and then iteratively learns from unlabeled datasets to gradually improve the segmentation performance. Self-Seg follows a semi-supervised teacher-student learning scheme and there are two contributions: 1) we construct a self-attention UNet to improve segmentation over the classical UNet model, and 2) we implement an automatic label grader to implicitly incorporate medical knowledge for quality assurance of pseudo labels, from which good quality pseudo labels are identified to enhance learning of the segmentation model. We perform extensive experiments on three CT image datasets and show promising results on five evaluation settings, and we also compared our method to several baseline and related methods and achieved superior performance.

Toward an Efficient Automatic Self-Augmentation Labeling Tool for Intrusion Detection Based on a Semi-Supervised Approach

Intrusion detection systems (IDSs) based on machine learning algorithms represent a key component for securing computer networks, where normal and abnormal behaviours of network traffic are automatically learned with no or limited domain experts’ interference. Most of existing IDS approaches rely on labeled predefined classes which require domain experts to efficiently and accurately identify anomalies and threats. However, it is very hard to acquire reliable, up-to-date, and sufficient labeled data for an efficient traffic intrusion detection model. To address such an issue, this paper aims to develop a novel self-automatic labeling intrusion detection approach (called SAL) which utilises only small labeled network traffic data to potentially detect most types of attacks including zero-day attacks. In particular, the proposed SAL approach has three phases including: (i) an ensemble-based decision-making phase to address the limitations of a single classifier by relying on the predictions of multi-classifiers, (ii) a function agreement phase to assign the class label based on an adaptive confidence threshold to unlabeled observations, and (iii) an augmentation labeling phase to maximise the accuracy and the efficiency of the intrusion detection systems in a classifier model and to detect new attacks and anomalies by utilising a hybrid voting-based ensemble learning approach. Experimental results on available network traffic data sets demonstrate that the proposed SAL approach achieves high performance in comparison to two well-known baseline IDSs based on machine learning algorithms.

Automatic macaque brain segmentation based on 7T MRI

BackgroundIn monkey neuroimaging, particularly magnetic resonance imaging (MRI) studies, quick and accurate automatic macaque brain segmentation is essential. However, there are few processing and analysis tools dedicated to automatic brain tissue segmentation and labeling of the macaque brain on a subject-specific basis. As a result, currently most adopted methods are through direct implementation of existing tools that have been designed for human brain. However, the operation steps combining different functional modules of a variety of processing and analysis tool software are inevitably complicated, cumbersome, time-consuming and labor-intensive. New methodIn this study, we proposed a novel quick and accurate automatic macaque brain segmentation method based on multi-atlas registration and majority-vote algorithm. First, the single-atlas method based on S-HAMMER is used to register each template image of the reference atlas set (including brain tissue labeled images and brain anatomical structure labeled images) to the preprocessed image to be segmented. Thus, we obtain the corresponding deformation field and spatially transform the labeled image, and then get multiple segmentation results by local weighted voting method, which perform label fusion to obtain the final labeled images of brain structures segmentation result. ResultsBy collecting high SNR and high spatial resolution images of macaque brain images from our 7T human MRI scanner, we have constructed two brain templates for each individual macaque subject, and macaque brain tissues and brain anatomical structure by one-atlas method. However, segmentation result of single-atlas method is not much accurate in some brain tissue area. It takes about 2 h and need more manual correction for segmentation. Automatic segmentation of macaque brain structure based on multi-atlas method was reasonably successful, the accuracy of segmentation was greatly improved without manual correction. Also, the proposed method provided good tissue fitting to V1 with smooth and continuous boundary. The Dice similarity of multi-atlas method showing 3.24%, 4.24%, 2.55%, 2.85%, 3.05%, and 0.35% improvement in image slices of 63, 66, 70, 71, 99 and 100, respectively. The entire processing time for the construction of a single template map took ~40 min. ConclusionsThis study proposed a novel automatic segmentation method of individual macaque brain structure based on multi-atlas registration method, which is concise and reliable. It may offer a valuable tool to applications in the field of brain and neuroscience research using the macaque as an experimental animal model.

A novel hybrid sand and dust storm detection method using MODIS data on GEE platform

ABSTRACT Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection.