Journal articles: 'Tail detection'

1

Xu, Yiming, Zhen Peng, Bin Shi, et al. "Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12972–80. https://doi.org/10.1609/aaai.v39i12.33415.

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The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-based models tend to excessively prioritize overall detection performance while neglecting robustness to structural imbalance, which can be problematic for many real-world networks following power-law degree distributions. Particularly, GCL-based methods may fail to capture tail anomalies (abnormal nodes with low degrees). This raises concerns about the security and robustness of current anomaly detection algorithms and therefore hinders their applicability in a variety of realistic high-risk scenarios. To the best of our knowledge, research on the robustness of graph anomaly detection to structural imbalance has received little scrutiny. To address the above issues, this paper presents a novel GCL-based framework named AD-GCL. It devises the neighbor pruning strategy to filter noisy edges for head nodes and facilitate the detection of genuine tail nodes by aligning from head nodes to forged tail nodes. Moreover, AD-GCL actively explores potential neighbors to enlarge the receptive field of tail nodes through anomaly-guided neighbor completion. We further introduce intra- and inter-view consistency loss of the original and augmentation graph for enhanced representation. The performance evaluation of the whole, head, and tail nodes on multiple datasets validates the comprehensive superiority of the proposed AD-GCL in detecting both head anomalies and tail anomalies.

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Yu, Junqin, Qiwen Wu, Kai Xie, et al. "Fish-Tail Bolt Loosening Detection Under Tilted Perspectives." Electronics 14, no. 7 (2025): 1281. https://doi.org/10.3390/electronics14071281.

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As a critical fastener connecting steel rails, fish-tail bolts ensure the safety of railway transportation. To improve the efficiency of fish-tail bolt loosening detection, this paper proposes a computer vision-based method for detecting fish-tail bolt looseness under tilted perspectives. The method first identifies bolt positions and coordinates of corner points on rail clamp edges through object detection and key point detection. Then, considering diverse rail clamp dimensions and combining with bolt positions, it employs dual perspective transformations for image rectification. Finally, utilizing the Lightweight OpenPose network, angle recognition of key bolt edges is achieved through Gaussian ring-shaped smooth labels, with loosening determination made by comparing angular variations across temporal frames. In experimental validation, tests were first conducted on a public dial-reading dataset for pointer angle recognition, showing a minimum average error of only 0.8°, which verifies the algorithm’s feasibility. Subsequently, based on fish-tail bolt images captured under various tilted perspectives, we constructed a self-made dataset of bolt key edges and performed loosening detection experiments. For bolt images in boundary postures, after rotation preprocessing, the average detection error was reduced to 0.7°. When the loosening threshold was set to 2.1°, the detection accuracy reached 97%. Experimental results indicate that the proposed method effectively identifies fish bolt loosening, providing crucial technical reference for railway safety maintenance.

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Ocepek, Marko, Anja Žnidar, Miha Lavrič, Dejan Škorjanc, and Inger Lise Andersen. "DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming." Agriculture 12, no. 1 (2021): 2. http://dx.doi.org/10.3390/agriculture12010002.

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The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

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Ocepek, Marko, Anja Žnidar, Miha Lavrič, Dejan Škorjanc, and Inger Lise Andersen. "DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming." Agriculture 12, no. 1 (2021): 2. https://doi.org/10.3390/agriculture12010002.

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The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

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Dr., Kailash Kumar. "Method of Oestrus Detection." Science World a monthly e magazine 3, no. 7 (2023): 1830–33. https://doi.org/10.5281/zenodo.8198575.

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1.     Different Methods of Heat Detection:- o   External signs of oestrus o   Bellowing o   Decreased feed intake / inappetance o   Frequent micturition o   Engorgement of teats o   Nervousness & excitement o   Stands to be mounted/mounts on fellow cows. o   Tilted tail- tail reflex o   Hypermic/oedematous & moist o   Vaginal mucus membrane-congested o   Clear copius, sticky mucus discharge

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Lin, Hang, Lixin Liu, and Zhengjun Zhang. "Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model." Mathematics 11, no. 14 (2023): 3194. http://dx.doi.org/10.3390/math11143194.

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Connecting derivative pricing with tail risk management has become urgent for financial practice and academia. This paper proposes a novel option pricing model based on the exponential generalized beta of the second kind (EGB2) distribution. The newly proposed model is of generality, simplicity, robustness, and financial interpretability. Most importantly, one can detect tail risk signals by calibrating the proposed model. The model includes the seminal Black–Scholes (B−S) formula as a limit case and can perfectly “replicate” the option prices from Merton’s jump-diffusion model. Based on the proposed pricing model, three tail risk warning measures are introduced for tail risk signals detection: the EGB2 implied tail index, the EGB2 implied Value-at-Risk (EGB2-VaR), and the EGB2 implied risk-neutral density (EGB2 R.N.D.). Empirical results show that the new pricing model can yield higher pricing accuracy than existing models in normal and crisis periods, and three model-based tail risk measures can perfectly detect tail risk signals before financial crises.

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Servin-Aguilar, Jesus G., Luis Rizo-Dominguez, Jorge A. Pardinas-Mir, Cesar Vargas-Rosales, and Ivan Padilla-Cantoya. "Epilepsy Seizure Detection: A Heavy Tail Approach." IEEE Access 8 (2020): 208170–78. http://dx.doi.org/10.1109/access.2020.3038397.

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Heseker, Philipp, Tjard Bergmann, Marc-Alexander Lieboldt, et al. "Exposing tail biters by automatic scream detection." Smart Agricultural Technology 9 (December 2024): 100582. http://dx.doi.org/10.1016/j.atech.2024.100582.

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Neuwinger, J., H. M. Behre, and E. Nieschlang. "Computerized semen analysis with sperm tail detection." Human Reproduction 5, no. 6 (1990): 719–23. http://dx.doi.org/10.1093/oxfordjournals.humrep.a137175.

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10

de Haan, Laurens, Albert Klein Tank, and Cláudia Neves. "On tail trend detection: modeling relative risk." Extremes 18, no. 2 (2014): 141–78. http://dx.doi.org/10.1007/s10687-014-0207-8.

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11

Jang, Jaeseok, and Hyuk-Yoon Kwon. "TAIL-MIL: Time-Aware and Instance-Learnable Multiple Instance Learning for Multivariate Time Series Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17582–89. https://doi.org/10.1609/aaai.v39i17.33933.

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This study addresses the challenge of detecting anomalies in multivariate time series data. Considering a bag (e.g., multi-sensor data) consisting of two-dimensional spaces of time points and multivariate instances (e.g., individual sensors), we aim to detect anomalies at both the bag and instance level with a unified model. To circumvent the practical difficulties of labeling at the instance level in such spaces, we adopt a multiple instance learning (MIL)-based approach, which enables learning at both the bag- and instance- levels using only the bag-level labels. In this study, we introduce time-aware and instance-learnable MIL (simply, TAIL-MIL). We propose two specialized attention mechanisms designed to effectively capture the relationships between different types of instances. We innovatively integrate these attention mechanisms with conjunctive pooling applied to the two-dimensional structure at different levels (i.e., bag- and instance-level), enabling TAIL-MIL to effectively pinpoint both the timing and causative multivariate factors of anomalies. We provide theoretical evidence demonstrating TAIL-MIL's efficacy in detecting instances with two-dimensional structures. Furthermore, we empirically validate the superior performance of TAIL-MIL over the state-of-the-art MIL methods and multivariate time-series anomaly detection methods.

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Deng, Liwei, Dexu Zhao, Qi Lan, and Fei Chen. "DOUNet: Dynamic Optimization and Update Network for Oriented Object Detection." Applied Sciences 14, no. 18 (2024): 8249. http://dx.doi.org/10.3390/app14188249.

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Object detection can accurately identify and locate targets in images, serving basic industries such as agricultural monitoring and urban planning. However, targets in remote sensing images have random rotation angles, which hinders the accuracy of remote sensing image object detection algorithms. In addition, due to the long-tailed distribution of detected objects in remote sensing images, the network finds it difficult to adapt to imbalanced datasets. In this article, we designed and proposed the Dynamic Optimization and Update network (DOUNet). By introducing adaptive rotation convolution to replace 2D convolution in the Region Proposal Network (RPN), the features of rotating targets are effectively extracted. To address the issues caused by imbalanced data, we have designed a long-tail data detection module to collect features of tail categories and guide the network to output more balanced detection results. Various experiments have shown that after two stages of feature learning and classifier learning, our designed network can achieve optimal performance and perform better in detecting imbalanced data.

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ISHII, K., H. ORIHARA, S. IWASAKI, K. SERA, S. FUTATSUGAWA, and Y. IWATA. "COMPTON TAIL BACKGROUND DUE TO SODIUM ELEMENT IN BIOLOGICAL SAMPLES." International Journal of PIXE 04, no. 02n03 (1994): 137–45. http://dx.doi.org/10.1142/s0129083594000180.

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The influence of Compton tail background on the PIXE detection limit is discussed in the case of human serum as an example of biological samples containing sodium element. The PIXE spectrum of the human serum for 3 MeV proton bombardment was measured with a Si(Li) x-ray detector and the Compton tail edge of 440keV γ-rays from 23Na in the serum was observed at the x-ray energy of 278.4 keV. The spectrum of the Compton tail was measured for the 511 keV γ-rays from a 22Na source and compared with a theoretical one. It is found that the intensity of the experimental Compton tail below 35 keV is two~three times larger than the theoretical one. Minimum detection limits for the elements of Z=35~55 in the human serum were estimated by using experimental Compton tail background.

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14

Sun, Ying Ke, and Xiao Lan Yao. "Research on Online Detection of Steel Plate Head and Tail Shape." Advanced Materials Research 909 (March 2014): 269–75. http://dx.doi.org/10.4028/www.scientific.net/amr.909.269.

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This paper aims to research the on-line measurement of the steel plate head and tail shape, and to measure the steel head and tail geometry size by using digital image processing and computer vision technology. The image gathered from high precision CCD camera is segmented with using the method based on the improved local binary fitting (LBF) model. The head and tail shape and size of steel plate is accurately measured, which provides the basis data for the follow-up steel rolling and shearing process.

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Liu, Bin, Cheng Zhou, and Xinsheng Zhang. "A tail adaptive approach for change point detection." Journal of Multivariate Analysis 169 (January 2019): 33–48. http://dx.doi.org/10.1016/j.jmva.2018.08.010.

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Wang, Daqing, Yifan Zhao, Wenjing Fang, et al. "Association of Sheep Tail Type with the T Gene Single Nucleotide Polymorphisms Loci." Life 15, no. 3 (2025): 342. https://doi.org/10.3390/life15030342.

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This study aimed to develop an effective tail typing detection technology based on the TaqMan probe technology for genotyping different sheep tail types. A total of 122 Hulun Buir short-tailed sheep and 50 Hu sheep were enrolled in the study to compare their tail morphologies, lengths, and widths. Through the Sanger sequencing of loci 333 and 334 in the second exon of the T gene, distinct genotypes of various types of Hulun Buir short-tailed sheep and Hu sheep were identified. In addition, the TaqMan probe technology was employed to genotype the two SNP loci of the T gene in the two types of sheep. It was observed that the second exon of the T gene in Hulun Buir short-tailed sheep at loci 333 and 334 exhibited two genotypes, CT/CT and CT/GG, but this feature was not detected for the T gene in Hu sheep. The detection accuracy of the TaqMan probe technology for sheep tail types exceeded 70%, suggesting that it is an effective tail-typing detection technology. This study provides a solid economic foundation and theoretical ideas that will improve the breeding of short-tailed sheep.

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Wei, Tong, Bo-Lin Wang, and Min-Ling Zhang. "EAT: Towards Long-Tailed Out-of-Distribution Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15787–95. http://dx.doi.org/10.1609/aaai.v38i14.29508.

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Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes. To overcome this issue, we propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes. This approach allows us to build a detector with clear decision boundaries by training on OOD data using virtual labels. (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data. This technique encourages the model to pay more attention to the discriminative features of the tail classes. We provide a clue for separating in-distribution and OOD data by analyzing gradient noise. Through extensive experiments, we demonstrate that our method outperforms the current state-of-the-art on various benchmark datasets. Moreover, our method can be used as an add-on for existing long-tail learning approaches, significantly enhancing their OOD detection performance. Code is available at: https://github.com/Stomach-ache/Long-Tailed-OOD-Detection.

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Aoki, Takahiro, Makoto Shibata, Guilherme Violin, Shogo Higaki, and Koji Yoshioka. "Detection of foaling using a tail-attached device with a thermistor and tri-axial accelerometer in pregnant mares." PLOS ONE 18, no. 6 (2023): e0286807. http://dx.doi.org/10.1371/journal.pone.0286807.

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It is desirable to attend to the mare at the time of foaling in order to assist fetal delivery and prevent complications. The early detection of the onset of labor is an important issue for the equine industry. The purpose of this study was to examine the applicability of a sensor for foaling detection using the data of surface temperature (ST), roll angle (rotation about the y-axis) and y-axis (long axis of the tail) acceleration which were collected from a multimodal device attached to the ventral tail base of the mare. The data were collected every 3 minutes in 17 pregnant mares. Roll angle differences from the reference values and the mare’s posture (standing or recumbent) confirmed by video were compared and associated. Cohen’s kappa coefficient was 0.99 when the threshold was set as ± 0.3 radian in roll angle differences. This result clearly showed that the sensor data can accurately distinguish between standing and recumbent postures. The hourly sensor data with a lower ST (LST < 35.5°C), a recumbent posture determined by the roll angle, and tail-raising (TR, decline of 200 mg or more from the reference value in y-axis acceleration) was significantly higher during the last hour prepartum than 2−120 hours before parturition (P < 0.01). The accuracy of foaling detection within one hour was verified using the following three indicators: LST; lying down (LD, change from standing to recumbent posture); and TR. When LST, LD and TR were individually examined, even though all indicators showed that sensitivity was 100%, the precision was 13.1%, 8.1% and 2.8%, respectively. When the data were combined as LST+LD, LST+TR, LD+TR and LST+LD+TR, detection of foaling improved, with precisions of 100%, 32.1%, 56.7% and 100%, respectively. In conclusion, the tail-attached multimodal device examined in this present study is useful for detecting foaling.

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Zhang, Tianjiao, Chaofan Ma, and Yanfeng Wang. "Tracking the Rareness of Diseases: Improving Long-Tail Medical Detection with a Calibrated Diffusion Model." Electronics 13, no. 23 (2024): 4693. http://dx.doi.org/10.3390/electronics13234693.

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Motivation: Chest X-ray (CXR) is a routine diagnostic X-ray examination for checking and screening various diseases. Automatically localizing and classifying diseases from CXR as a detection task is of much significance for subsequent diagnosis and treatment. Due to the fact that samples of some diseases are difficult to acquire, CXR detection datasets often present a long-tail distribution over different diseases. Objective: The detection performance of tail classes is very poor due to the limited number and diversity of samples in the training dataset and should be improved. Method: In this paper, motivated by a correspondence-based tracking system, we build a pipeline named RaTrack, leveraging a diffusion model to alleviate the tail class degradation problem by aligning the generation process of the tail to the head class. Then, the samples of rare classes are generated to extend the number and diversity of rare samples. In addition, we propose a filtering strategy to control the quality of the generated samples. Results: Extensive experiments on public datasets, Vindr-CXR and RSNA, demonstrate the effectiveness of the proposed method, especially for rare diseases.

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Gao, Jiayi, Kongming Liang, Tao Wei, Wei Chen, Zhanyu Ma, and Jun Guo. "Dual-Prior Augmented Decoding Network for Long Tail Distribution in HOI Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 1806–14. http://dx.doi.org/10.1609/aaai.v38i3.27949.

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Human object interaction detection aims at localizing human-object pairs and recognizing their interactions. Trapped by the long-tailed distribution of the data, existing HOI detection methods often have difficulty recognizing the tail categories. Many approaches try to improve the recognition of HOI tasks by utilizing external knowledge (e.g. pre-trained visual-language models). However, these approaches mainly utilize external knowledge at the HOI combination level and achieve limited improvement in the tail categories. In this paper, we propose a dual-prior augmented decoding network by decomposing the HOI task into two sub-tasks: human-object pair detection and interaction recognition. For each subtask, we leverage external knowledge to enhance the model's ability at a finer granularity. Specifically, we acquire the prior candidates from an external classifier and embed them to assist the subsequent decoding process. Thus, the long-tail problem is mitigated from a coarse-to-fine level with the corresponding external knowledge. Our approach outperforms existing state-of-the-art models in various settings and significantly boosts the performance on the tail HOI categories. The source code is available at https://github.com/PRIS-CV/DP-ADN.

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Liu, Wei, Kai He, Qun Gao, and Cheng-yin Liu. "Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/283606.

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Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new vibration signal analysis approach to detecting the coal-gangue interface based on singular value decomposition (SVD) techniques and support vector machines (SVMs). Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD) is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices can be formed automatically. By applying the SVD algorithm to the initial feature vector matrices, the singular values of matrices can be obtained and used as the input feature vectors of SVMs classifier. The analysis results of vibration signals from the tail boom support of a longwall mining machine show that the method based on EMD, SVD, and SVM is effective for coal-gangue interface detection even when the number of samples is small.

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Wang, Weitao, Meng Wang, Sen Wang, et al. "One-Shot Learning for Long-Tail Visual Relation Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12225–32. http://dx.doi.org/10.1609/aaai.v34i07.6904.

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The aim of visual relation detection is to provide a comprehensive understanding of an image by describing all the objects within the scene, and how they relate to each other, in < object-predicate-object > form; for example, < person-lean on-wall > . This ability is vital for image captioning, visual question answering, and many other applications. However, visual relationships have long-tailed distributions and, thus, the limited availability of training samples is hampering the practicability of conventional detection approaches. With this in mind, we designed a novel model for visual relation detection that works in one-shot settings. The embeddings of objects and predicates are extracted through a network that includes a feature-level attention mechanism. Attention alleviates some of the problems with feature sparsity, and the resulting representations capture more discriminative latent features. The core of our model is a dual graph neural network that passes and aggregates the context information of predicates and objects in an episodic training scheme to improve recognition of the one-shot predicates and then generate the triplets. To the best of our knowledge, we are the first to center on the viability of one-shot learning for visual relation detection. Extensive experiments on two newly-constructed datasets show that our model significantly improved the performance of two tasks PredCls and SGCls from 2.8% to 12.2% compared with state-of-the-art baselines.

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Abbasi, Abdul Muqtadir, Mustafa Al-Tekreeti, Kshirasagar Naik, Amiya Nayak, Pradeep Srivastava, and Marzia Zaman. "Characterization and Detection of Tail Energy Bugs in Smartphones." IEEE Access 6 (2018): 65098–108. http://dx.doi.org/10.1109/access.2018.2877395.

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Filik, Karolina, Bożena Szermer-Olearnik, Sabina Oleksy, Jan Brykała, and Ewa Brzozowska. "Bacteriophage Tail Proteins as a Tool for Bacterial Pathogen Recognition—A Literature Review." Antibiotics 11, no. 5 (2022): 555. http://dx.doi.org/10.3390/antibiotics11050555.

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In recent years, a number of bacterial detection methods have been developed to replace time-consuming culture methods. One interesting approach is to mobilize the ability of phage tail proteins to recognize and bind to bacterial hosts. In this paper, the authors provide an overview of the current methodologies in which phage proteins play major roles in detecting pathogenic bacteria. Authors focus on proteins capable of recognizing highly pathogenic strains, such as Acinetobacter baumannii, Campylobacter spp., Yersinia pestis, Pseudomonas aeruginosa, Listeria monocytogenes, Staphylococcus aureus, Enterococcus spp., Salmonella spp., and Shigella. These pathogens may be diagnosed by capture-based detection methods involving the use of phage protein-coated nanoparticles, ELISA (enzyme-linked immunosorbent assay)-based methods, or biosensors. The reviewed studies show that phage proteins are becoming an important diagnostic tool due to the discovery of new phages and the increasing knowledge of understanding the specificity and functions of phage tail proteins.

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Peters, Richard A. "Environmental motion delays the detection of movement-based signals." Biology Letters 4, no. 1 (2007): 2–5. http://dx.doi.org/10.1098/rsbl.2007.0422.

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Animal signals are constrained by the environment in which they are transmitted and the sensory systems of receivers. Detection of movement-based signals is particularly challenging against the background of wind-blown plants. The Australian lizard Amphibolurus muricatus has recently been shown to compensate for greater plant motion by prolonging the introductory tail-flicking component of its movement-based display. Here I demonstrate that such modifications to signal structure are useful because environmental motion lengthens the time lizard receivers take to detect tail flicks. The spatio-temporal properties of animal signals and environmental motion are thus sufficiently similar to make signal detection more difficult. Environmental motion, therefore, must have had an influence on the evolution of movement-based signals and motion detection mechanisms.

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Kauselmann, Karen, E. Tobias Krause, Hansjörg Schrade, and Lars Schrader. "Retrospective exploratory evaluation of individual pigs’ behaviour involved in tail biting during rearing and fattening." PLOS ONE 20, no. 1 (2025): e0316044. https://doi.org/10.1371/journal.pone.0316044.

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Tail biting is one of the biggest welfare problems in pigs. However, depending on the individuals involved (e.g., tail biter/victim), pigs seem to change their behaviour prior to tail biting events, which raises the possibility of early detection and thus prediction and prevention of tail biting. In this retrospective explorative study, we used datasets from four different studies with 9 trials of rearing (4 pens/trial with 24 pigs/pen) and fattening (8 pens/trial with 12 pigs/pen) that focused on the exploration behaviour of undocked pigs towards plant-based enrichment materials. From this dataset, we identified 8 pens from rearing (n = 192 pigs) and 6 pens from fattening (n = 72 pigs) in which individual tail biters were identified. From this dataset, we investigated whether any a priori behavioural changes in exploration or feeding could be identified with respect to tail biting. Furthermore, the effects of weight parameters from suckling to fattening were examined. Using linear mixed effects models, we found that exploration duration was linked to days prior to tail biting in rearing, depending on CatPig (category of pigs: biter, victim, neutral pig) (P = 0.001), in fattening independent of CatPig (P<0.0001), and by duration, amount and frequency of feed consumption in fattening (P<0.0001). Some weight parameters covaried with CatPig in rearing (weight-gain suckling: P = 0.0018; weaning weight: P = 0.019) and fattening (weaning weight: P = 0.07; start weight at fattening: P = 0.03; weight-gain rearing: P = 0.02). Suitable indicators for future early detection trials of tail biting could be exploration duration in rearing and fattening and feeding data in fattening. Moreover, weight parameters in rearing and fattening and exploration duration in rearing may be used to identify individual pigs that might become tail biters in an upcoming tail biting event. The retrospective explorative nature of our analysis revealed interesting patterns; however, further studies are needed to confirm our findings.

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He, Yina, Lei Peng, Yongcun Zhang, Juanjuan Weng, Shaozi Li, and Zhiming Luo. "Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to Tail." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 3446–54. https://doi.org/10.1609/aaai.v39i3.32357.

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Current out-of-distribution (OOD) detection methods typically assume balanced in-distribution (ID) data, while most real-world data follow a long-tailed distribution. Previous approaches to long-tailed OOD detection often involve balancing the ID data by reducing the semantics of head classes. However, this reduction can severely affect the classification accuracy of ID data. The main challenge of this task lies in the severe lack of features for tail classes, leading to confusion with OOD data. To tackle this issue, we introduce a novel Prioritizing Attention to Tail (PATT) method using augmentation instead of reduction. Our main intuition involves using a mixture of von Mises-Fisher (vMF) distributions to model the ID data and a temperature scaling module to boost the confidence of ID data. This enables us to generate infinite contrastive pairs, implicitly enhancing the semantics of ID classes while promoting differentiation between ID and OOD data. To further strengthen the detection of OOD data without compromising the classification performance of ID data, we propose feature calibration during the inference phase. By extracting an attention weight from the training set that prioritizes the tail classes and reduces the confidence in OOD data, we improve the OOD detection capability. Extensive experiments verified that our method outperforms the current state-of-the-art methods on various benchmarks.

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Zhu, Wenbo, Xinghao Zhang, Zhengjun Zhu, Weijie Fu, Neng Liu, and Zhengquan Zhang. "A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction." Mathematics 12, no. 11 (2024): 1685. http://dx.doi.org/10.3390/math12111685.

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Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models’ oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks.

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Bac, Mehmet. "Crime Chains." American Economic Journal: Microeconomics 14, no. 4 (2022): 680–722. http://dx.doi.org/10.1257/mic.20200314.

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How should law enforcement resources be allocated to minimize the harms from flexible, chain-form trafficking organizations? I show that optimal interventions focus on one target, the feeding source (decapitation) or the revenue-generating tail (amputation). Decapitation dismantles the crime chain under large budgets but induces maximal expansion otherwise, whereas amputation generates a rich set of detection outcomes and limits the chain’s size response. A rule of thumb emerges for authorities to target tail segments under small budgets and high detection contiguity, qualified by chain profitability and enforcement parameters. Real-world interventions fail to coordinate on such efficient targeting. (JEL K42)

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Liu, Bo Hang, Hong Yang Zhu, and Wen Sheng Zhang. "The Sedan Length Detection Algorithm Based on the Tail Features." Applied Mechanics and Materials 543-547 (March 2014): 917–21. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.917.

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Extracting effectively the vehicle length is beneficial to classification of vehicle in video traffic detection. In the traditional video traffic detection, vehicles can be approximately regarded as a rectangle to extract the vehicle length. However, it is not accurate enough to use rectangle method to extract the length of sedan. In this paper, based on the tail features of the sedan, the mathematical algorithm of trigonometric function and similar triangles were applied to calculate the sedan length. And then, we discuss the computing method of sedan length detected by video from the different camera angles and detection line locations, and analyze the influence on the accuracy of the measuring sedan length. At last, a study is conducted for example analysis, and the result shows that the approach of extracting sedan length is feasible. Compared with the traditional algorithm, the sedan length accuracy is improved by 3.18% using the algorithm in the paper. Thus, the algorithm is of great significance to improve the accuracy of measuring the sedan length.

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Liu, Cong, Wuping Zhou, Tao Zhang, Keming Jiang, Haiwen Li, and Wenfei Dong. "An automated approach to classification of duplex assay for digital droplet PCR." Journal of Bioinformatics and Computational Biology 16, no. 03 (2018): 1850003. http://dx.doi.org/10.1142/s0219720018500038.

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In the digital polymerase chain reaction (dPCR) detection process, discriminating positive droplets from negative ones directly affects the final concentration and is one of the most important factors affecting accuracy. Current automated classification methods usually discuss single-channel detections, whereas duplex detection experiments are less discussed. In this paper, we designed a classification method by estimating the upper limit of the negative droplets. The right tail of the negative droplets is approximated using a generalized Pareto distribution. Furthermore, our method takes fluorescence compensation in duplex assays into account. We also demonstrate the method on Bio-Rad’s mutant detection dataset. Experimental results show that the method provides similar or better accuracy than other algorithms reported over a wider dynamic range.

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Wang, Xiao-tao, Hua Qian, Jing Xu, and Yang Yang. "Trap Detection Based Decoding Algorithm for Tail-biting Convolutional Codes." Journal of Electronics & Information Technology 33, no. 10 (2011): 2300–2305. http://dx.doi.org/10.3724/sp.j.1146.2011.00413.

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Efrat, Z., T. Perri, I. Meizner, R. Chen, Z. Ben-Rafael, and A. Dekel. "Early sonographic detection of a ‘human tail’: a case report." Ultrasound in Obstetrics and Gynecology 18, no. 5 (2001): 534–35. http://dx.doi.org/10.1046/j.0960-7692.2001.568.doc.

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Comer, Jeffrey. "Chasing a Protein’s Tail: Detection of Polypeptide Translocation through Nanopores." Biophysical Journal 114, no. 4 (2018): 759–60. http://dx.doi.org/10.1016/j.bpj.2017.12.020.

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Ollagnier, Catherine, Claudia Kasper, Anna Wallenbeck, Linda Keeling, Giuseppe Bee, and Siavash A. Bigdeli. "Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records." PLOS ONE 18, no. 1 (2023): e0252002. http://dx.doi.org/10.1371/journal.pone.0252002.

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Tail biting is a damaging behaviour that impacts the welfare and health of pigs. Early detection of precursor signs of tail biting provides the opportunity to take preventive measures, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real-time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (Generalized Linear Model with Stepwise Feature Selection, random forest, Support Vector Machines with Radial Basis Function Kernel, Bayesian Generalized Linear Model, Neural network, K-nearest neighbour, and Partial Least Squares Discriminant Analysis) were evaluated from daily feeding data collected from 65 pens originating from two herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data in two different ways, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. In the first data splitting, the model is regularly updated with previous data from the pen, whereas in the second data splitting, the model tries to predict for a pen that it has never seen before. The K-nearest neighbour algorithm was able to predict 78% of the upcoming events with an accuracy of 96%, when predicting events in pens for which it had previous data. Our results indicate that machine learning models can be considered for implementation into automatic feeder systems for real-time prediction of tail biting events.

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Sasaki, Yosuke, Yoshihiro Iki, Tomoaki Anan, Jun Hayashi, and Mizuho Uematsu. "Assessment of Ventral Tail Base Surface Temperature for the Early Detection of Japanese Black Calves with Fever." Animals 13, no. 3 (2023): 469. http://dx.doi.org/10.3390/ani13030469.

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The objective in the present study was to assess the ventral tail base surface temperature (ST) for the early detection of Japanese Black calves with fever. This study collected data from a backgrounding operation in Miyazaki, Japan, that included 153 calves aged 3–4 months. A wearable wireless ST sensor was attached to the surface of the ventral tail base of each calf at its introduction to the farm. The ventral tail base ST was measured every 10 min for one month. The present study conducted an experiment to detect calves with fever using the estimated residual ST (rST), calculated as the estimated rST minus the mean estimated rST for the same time on the previous 3 days, which was obtained using machine learning algorithms. Fever was defined as an increase of ≥1.0 °C for the estimated rST of a calf for 4 consecutive hours. The machine learning algorithm that applied was a random forest, and 15 features were included. The variable importance scores that represented the most important predictors for the detection of calves with fever were the minimum and maximum values during the last 3 h and the difference between the current value and 24- and 48-h minimum. For this prediction model, accuracy, precision, and sensitivity were 98.8%, 72.1%, and 88.1%, respectively. The present study indicated that the early detection of calves with fever can be predicted by monitoring the ventral tail base ST using a wearable wireless sensor.

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Li, Jia, Pei Wu, Feilong Kang, Lina Zhang, and Chuanzhong Xuan. "Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis." Advances in Multimedia 2018 (October 10, 2018): 1–8. http://dx.doi.org/10.1155/2018/9106836.

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The study of the self-protective behaviors of dairy cows suffering dipteral insect infestation is important for evaluating the breeding environment and cows’ selective breeding. The current practices for measuring diary cows’ self-protective behaviors are mostly by human observation, which is not only tedious but also inefficient and inaccurate. In this paper, we develop an automatic monitoring system based on video analysis. First, an improved optical flow tracking algorithm based on Shi-Tomasi corner detection is presented. By combining the morphological features of head, leg, and tail movements, this method effectively reduces the number of Shi-Tomasi points, eliminates interference from background movement, reduces the computational complexity of the algorithm, and improves detection accuracy. The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. The method proposed in this paper which provides objective measurements can help researchers to more effectively analyze dairy cows’ self-protective behaviors and the living environment in the process of dairy cow breeding and management.

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Mohanarangan R Kalaivani, S. "Enhanced Congestion Control in Wireless Networks: A Joint Random Early Detection and Drop Tail Mechanism." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 734–41. http://dx.doi.org/10.21275/sr23801191338.

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G. Mamekal, Modarissa, B. T. Agullana, P. J. J. Belnas, et al. "Early Detection of Livestock Fever, Estrus, and Parturition Using Wearable Tail Sensor." International Research Journal of Innovations in Engineering and Technology 08, no. 07 (2024): 71–81. http://dx.doi.org/10.47001/irjiet/2024.807007.

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The advancement of technology paved the way for the wearable tail sensor to be developed as livestock owners depend on analog measurements for detecting fever, estrus, and parturition. Hence, these advancements help farm and livestock management. This study aimed to design and develop a wearable tail sensor that integrates heat, motion, and pulse sensors for the early detection of livestock fever, estrus, and parturition. This study utilized a Research and Development (R&D) design employing the 4D Model of device development, which encompasses four distinct phases: define, design, develop, and disseminate. The study successfully designed and developed a device that integrates four sensors to detect fever, estrus, and parturition in livestock. The results revealed that the device has a high level of acceptability and adaptability. Statistical analysis also showed that there is no significant difference between the measurements of the analog instruments and the wearable tail sensor in terms of temperature and pulse rate for both cattle and pigs. The researchers successfully constructed a wearable device integrating four sensors to detect fever, estrus, and parturition in livestock that transmits real-time data over the web server and accurate measurements of temperature, pulse, and motion of livestock. This advancement benefits the livestock owners by making it less difficult to track the livestock’s fever, estrus, and parturition.

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Nair, Sumesh, Guo-Fong Hong, Chia-Wei Hsu, Chun-Yu Lin, and Shean-Jen Chen. "Real-Time Caterpillar Detection and Tracking in Orchard Using YOLO-NAS Plus SORT." Agriculture 15, no. 7 (2025): 771. https://doi.org/10.3390/agriculture15070771.

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Detecting and tracking caterpillars in orchard environments is crucial for advancing precision agriculture but remains challenging due to occlusions, variable lighting, wind interference, and the need for precise small-object detection. This study presents a real-time deep learning approach that integrates the YOLO-NAS object detection model with the SORT tracking algorithm to overcome these challenges. Evaluated in a jujube orchard, the proposed method significantly improved small caterpillar detection and tracking. Using an RGB-D camera operating at 30 frames per second, the system successfully detected caterpillars measuring 2–5 cm at distances of 20–35 cm, corresponding to resolutions of 21 × 6 to 55 × 10 pixels. The integration of YOLO-NAS with SORT enhanced detection performance, achieving a ~9% increase in true positive detections and an ~8% reduction in false positives compared to YOLO-NAS alone. Even for the smallest caterpillars (21 × 6 pixels), the method achieved over 60% true positive detection accuracy without false positives within 1 s inference. With an inference time of just 0.2 milliseconds, SORT enabled real-time tracking and accurately predicted caterpillar positions under wind interference, further improving reliability. Additionally, selective corner tracking was employed to identify the head and tail of caterpillars, paving the way for future laser-based precision-targeting interventions focused on the caterpillar head.

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Zaman, Yasir, Vineet Tirth, Nasir Rahman, et al. "Electrical and Optical Properties of Indium and Lead Co-Doped Cd0.9Zn0.1Te." Materials 14, no. 19 (2021): 5825. http://dx.doi.org/10.3390/ma14195825.

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We have investigated the electrical and optical properties of Cd0.9Zn0.1Te:(In,Pb) wafers obtained from the tip, middle, and tail of the same ingot grown by modified vertical Bridgman method using I-V measurement, Hall measurement, IR Transmittance, IR Microscopy and Photoluminescence (PL) spectroscopy. I-V results show that the resistivity of the tip, middle, and tail wafers are 1.8 × 1010, 1.21 × 109, and 1.2 × 1010 Ω·cm, respectively, reflecting native deep level defects dominating in tip and tail wafers for high resistivity compared to the middle part. Hall measurement shows the conductivity type changes from n at the tip to p at the tail in the growth direction. IR Transmittance for tail, middle, and tip is about 58.3%, 55.5%, and 54.1%, respectively. IR microscopy shows the density of Te/inclusions at tip, middle, and tail are 1 × 103, 6 × 102 and 15 × 103/cm2 respectively. Photoluminescence (PL) spectra reflect that neutral acceptor exciton (A0,X) and neutral donor exciton (D0,X) of tip and tail wafers have high intensity corresponding to their high resistivity compared to the middle wafer, which has resistivity a little lower. These types of materials have a large number of applications in radiation detection.

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Xiao, Shuyu, Yaming Jin, Xiaomei Lu, et al. "Dynamics and manipulation of ferroelectric domain walls in bismuth ferrite thin films." National Science Review 7, no. 2 (2019): 278–84. http://dx.doi.org/10.1093/nsr/nwz176.

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Abstract Ferroelectric domain walls differ from domains not only in their crystalline and discrete symmetry, but also in their electronic, magnetic, and mechanical properties. Although domain walls provide a degree of freedom to regulate the physical properties at the nanoscale, the relatively lower controllability prevents their practical applications in nano-devices. In this work, with the advantages of 3D domain configuration detection based on piezoresponse force microscopy, we find that the mobility of three types of domain walls (tail-to-tail, head-to-tail, head-to-head) in (001) BiFeO3 films varies with the applied electrical field. Under low voltages, head-to-tail domain walls are more mobile than other domain walls, while, under high voltages, tail-to-tail domain walls become rather active and possess relatively long average lengths. This is due to the high nucleation energy and relatively low growth energy for charged domain walls. Finally, we demonstrate the manipulation of domain walls through successive electric writings, resulting in well-aligned conduction paths as designed, paving the way for their application in advanced spintronic, memory and communication nano-devices.

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Hsieh, Ting-I., Esther Robb, Hwann-Tzong Chen, and Jia-Bin Huang. "DropLoss for Long-Tail Instance Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1549–57. http://dx.doi.org/10.1609/aaai.v35i2.16246.

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Long-tailed class distributions are prevalent among the practical applications of object detection and instance segmentation. Prior work in long-tail instance segmentation addresses the imbalance of losses between rare and frequent categories by reducing the penalty for a model incorrectly predicting a rare class label. We demonstrate that the rare categories are heavily suppressed by correct background predictions, which reduces the probability for all foreground categories with equal weight. Due to the relative infrequency of rare categories, this leads to an imbalance that biases towards predicting more frequent categories. Based on this insight, we develop DropLoss -- a novel adaptive loss to compensate for this imbalance without a trade-off between rare and frequent categories. With this loss, we show state-of-the-art mAP across rare, common, and frequent categories on the LVIS dataset. Codes are available at https://github.com/timy90022/DropLoss.

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Zhang, Xiao Hui, Qing Liu, and Yun Hou. "Preceding Vehicle Visual Detection and Tracking Based on Template and Shadow Feature." Advanced Materials Research 490-495 (March 2012): 481–85. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.481.

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As to the vehicle visual detection and tracking problem in IDASWs (Intelligent Driver-Assistance and Safety Warning System), this paper proposed a way of a good effect. Firstly use vehicle’s bottom shadow feature and identified lane result to determine the searching region. Then use template matching algorithm based on vehicle’s tail shape to realize detecting and tracking. Experimental results show that the algorithm can effectively detect and track the preceding vehicle from the video sequence, reaching robust and real-time requirements

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Al-Amili, Wiaam Ahmed. "Evaluation of DNA Damage for Some Iraqi Workers Employ in a Wooden Furniture Factories." Journal of Kerbala for Agricultural Sciences 4, no. 5 (2017): 221–28. http://dx.doi.org/10.59658/jkas.v4i5.706.

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Wood is among the most universe substantial resources and one of the main common kinds of occupational exposure. For this , the research goal was the assess of the genotoxicity effects of wood dust exposure for some Iraqi workers employed in a wooden furniture factories using alkaline comet assay based on measuring the DNA damage that occurred in the white blood cells (WBC) , aimed to minimize the health risks from dangerous substances in the workplace. Fifty workers in a wooden furniture factories and 50 apparently healthy control were used in this study. DNA damage was significantly higher in the wood's workers , than in the control subjects according to the comet parameters. Thus , significantly higher levels of DNA damage observed in wood's workers in whom either smoked ( tail length 40.15 ± 0.54; tail DNA % 32.12 ± 0.87 and tail moment 12.90 ± 0.98) or non-smokers (tail length 36.21 ± 0.43 ; tail DNA % 29.56 ± 0.34 and tail moment 10.70 ± 0.10) than in smoker (tail length 12.81 ± 0.89; tail DNA % 6.60 ± 0.06 and tail moment 0.846 ± 0.02) and non-smokers (tail length 2.63 ± 0.33 ; tail DNA % 2.29 ± 0.07 and tail moment 0.060 ± 0.03) of control group. Present study deduce the relationship of the exposure to wood dust with high level of DNA damage. In conclusion, the results indicated that there was a possibility of using the changes in the level of comet assay as biodosimetry for the detection of DNA damage of workers employed in a wooden furniture factories . Also, the results obtaining was confirmed by usefulness of the alkaline comet assay as a sensitive additional marker in the regular health screening of workers occupationally exposed to dangerous wooden dust .

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Chatha, Ahmad Manan Mustafa, Saima Naz, Syeda Saira Iqbal, et al. "Detection of DNA Damage in Fish using Comet Assay." Current Trends in OMICS 4, no. 1 (2023): 01–16. http://dx.doi.org/10.32350/cto.41.01.

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Heavy metals have an enduring presence, risky characteristics, and the propensity to accumulate in the environment. This is why heavy metal toxics are widely acknowledged as harmful environmental pollutants. Heavy metals damage both aquatic and terrestrial ecosystems, posing a major risk to the environment and human health. Four freshwater fish species namely Labeo rohita, Catla catla, Hypophthalmichthys molitrix, and Ctenopharyngodon idella were the focus of this investigation. This study investigated the potential genotoxic effects of lead (Pb), copper (Cu), and cadmium (Cd) on the above fish species through the application of comet assay test. The fish were exposed to these metals at four distinct concentrations (19%, 24%, 31%, and 50% of the LC50) over the course of 40 days. All four fish species were exposed to metals to varying degrees, according to the genetic damage index, cumulative tail length of comets, and the proportion of damaged cells. In contrast to Catla catla, Hypophthalmichthys molitrix had the highest prevalence of DNA damage. The current study suggests that the presence of these particular metals in Pakistan's aquatic ecosystems may have an adverse effect on the DNA of the country's fish species. Metals cause damage to DNA in fibroblast cells through distinct mechanisms when present in water, air, and soil. Comet assay test has a remarkable sensitivity that helps to identify extremely low amounts of DNA damage. Out of the four fish species, Ctenopharyngodon idella showed higher levels of damaged cells, a higher genetic damage index, and a cumulative comet tail length as compared to others. All four fish species experienced a significant increase in DNA damage, genetic damage index, and comet tail length at 50% concentration of metals LC50.

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Yu, Hou Yun, and Wei Gong Zhang. "Visual Detection of Moving Vehicles Ahead Based on the Characteristics." Applied Mechanics and Materials 103 (September 2011): 165–69. http://dx.doi.org/10.4028/www.scientific.net/amm.103.165.

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Machine vision perception technology is widely used in the vehicle’s active safety system. It provides more immediate and correct information of road and vehicles around, in which inspection of moving vehicle ahead is one of the important items. A method of inspection fused of detection of the shadow under the vehicle and symmetry of the vehicle’s tail is presented in this paper. At first, a region of interest is selected according to the lane lines. Then, the shadow can be detected with grayscale histogram in the region of interest and a suspected area of vehicle is obtained by expanding the shadow with empirical proportion. At last, the vehicle ahead is further affirmed by calculating the symmetry of such characteristic at its tail as grayscale value, taillight and the edges. Experimental results prove that this method can well solve the actual problems of vehicle detection.

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Jiang, Guo Ping, Zheng Guo Zhang, Zhi Liang Jin, and Tao Zhao. "The Pilot Plant Design and Operation Scheme Research on Treatment of Fluoride Flue Gas by Wet Absorption in Electrolytic Aluminium Industry." Advanced Materials Research 997 (August 2014): 633–37. http://dx.doi.org/10.4028/www.scientific.net/amr.997.633.

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A survey on the tail gas contained HF of the electrolyzed aluminumindustry was analyzed at first,which included the applications of tail gas treating technology and gas purification devices both at home and abroad.Combining with their professional knowledge and engineering experience,a technical scheme was formed that purifying the tail gas with HF by a new progress,which designed a set of pilot plant about wet hydrogen fluoride absorption, and the optimization of this process scheme. At last,safe operation, equipment design and material selection, automatic control were discussed in this paper. It is worth mentioning that the detection method of by-products and the comprehensive utilization of subsequent product were established.

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Ainsleigh, Phillip L. "Acoustic echo detection and arrival-time estimation using spectral tail energy." Journal of the Acoustical Society of America 110, no. 2 (2001): 967–72. http://dx.doi.org/10.1121/1.1381027.

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He, Lin, Zhuliang Yu, Zhenghui Gu, and Yuanqing Li. "Long-tail distribution based multiscale-multiband autoregressive detection for hyperspectral imagery." Multidimensional Systems and Signal Processing 24, no. 1 (2011): 65–85. http://dx.doi.org/10.1007/s11045-011-0155-2.

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Journal articles on the topic 'Tail detection'

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