Relevant bibliographies by topics / Time series outlier detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Time series outlier detection'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Time series outlier detection"

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MENGUTAYCI, Ümmügülsüm, and Selma Ayşe ÖZEL. "An Outlier Analysis on Multi-Dimensional and Time-Series Data." AINTELIA SCIENCE NOTES 1, no. 1 (2022): 162–68. https://doi.org/10.5281/zenodo.8071461.

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Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, hacking, mislabeled data, or unusual behavior in the system. Therefore, it is important to determine these values. In this study, outlier detection performances of the algorithms used in outlier detection analysis on different types of data sets were calculated and compared. As a result of the study, it was seen that the algorithms showed sufficient success. The highest performance was seen in the Histogram-based outlier detection algorithm with 99 % accuracy.
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Twumasi-Ankrah, Sampson, Simon Kojo Appiah, Doris Arthur, Wilhemina Adoma Pels, Jonathan Kwaku Afriyie, and Danielson Nartey. "Comparison of outlier detection techniques in non-stationary time series data." Global Journal of Pure and Applied Sciences 27, no. 1 (2021): 55–60. http://dx.doi.org/10.4314/gjpas.v27i1.7.

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This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques u
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Ji, Yanjie, Dounan Tang, Weihong Guo, Phil T. Blythe, and Gang Ren. "Detection of Outliers in a Time Series of Available Parking Spaces." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/416267.

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With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrins
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Choi, Jeong In, In Ok Um, and Hyung Jun Choa. "Outlier detection in time series data." Korean Journal of Applied Statistics 29, no. 5 (2016): 907–20. http://dx.doi.org/10.5351/kjas.2016.29.5.907.

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Choy, Kokyo. "Outlier detection for stationary time series." Journal of Statistical Planning and Inference 99, no. 2 (2001): 111–27. http://dx.doi.org/10.1016/s0378-3758(01)00081-7.

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Abraham, Bovas, and Alice Chuang. "Outlier Detection and Time Series Modeling." Technometrics 31, no. 2 (1989): 241–48. http://dx.doi.org/10.1080/00401706.1989.10488517.

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Ljung, Greta M. "On Outlier Detection in Time Series." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 2 (1993): 559–67. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01924.x.

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Chung, Se Yeon, and Sang Cheol Kim. "Anomaly Detection in Livestock Environmental Time Series Data Using LSTM Autoencoders: A Comparison of Performance Based on Threshold Settings." Korean Institute of Smart Media 13, no. 4 (2024): 48–56. http://dx.doi.org/10.30693/smj.2024.13.4.48.

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In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detect
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Nguyen, Huy Dinh, and Trong Dinh Tran. "Detecting outliers in GNSS position time series using machine learning techniques." Journal of Mining and Earth Sciences 64, no. 4 (2023): 22–30. http://dx.doi.org/10.46326/jmes.2023.64(4).03.

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The Global Navigation Satellite System (GNSS) position time series is applied in studies that require high-precision positioning, such as monitoring tectonic movements and Earth deformation. Outliers in GNSS position time series can significantly impact the accuracy of station positioning and movement parameters, leading to distorted data analysis outcomes. This study investigates the effectiveness of three machine learning techniques, including-Isolation Forest, One-Class Support Vector Machines (O-C SVM), and Local Outlier Factor (LOF) for outlier detection in GNSS position time series, with
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Lee, Jun-Whan, Sun-Cheon Park, Duk Kee Lee, and Jong Ho Lee. "Tsunami arrival time detection system applicable to discontinuous time series data with outliers." Natural Hazards and Earth System Sciences 16, no. 12 (2016): 2603–22. http://dx.doi.org/10.5194/nhess-16-2603-2016.

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Abstract. Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami de
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Dissertations / Theses on the topic "Time series outlier detection"

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Sedman, Robin. "Online Outlier Detection in Financial Time Series." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228069.

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In this Master’s thesis, different models for outlier detection in financial time series are examined. The financial time series are price series such as index prices or asset prices. Outliers are, in this thesis, defined as extreme and false points, but this definition is also investigated and revised. Two different time series models are examined: an autoregressive (AR) and a generalized autoregressive conditional heteroskedastic (GARCH) time series model, as well as one test statistic method based on the GARCH model. Additionally, a nonparametric model is examined, which utilizes kernel density e
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Wang, Dan Tong. "Outlier detection with data stream mining approach in high-dimenional time series data." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691091.

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Bergamelli, Michele. "Structural breaks and outliers detection in time-series econometrics : methods and applications." Thesis, City University London, 2015. http://openaccess.city.ac.uk/14868/.

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This thesis contributes to the econometric literature on structural breaks analysis and outliers detection in parametric linear models. The focus is on the development of new econometric tools as well as on the analysis of novel but largely unexplored approaches. The econometric methods under analysis are illustrated using macroeconomic and financial relationships. The thesis is organised in three main chapters. In Chapter 2, we consider two novel methods to detect multiple structural breaks affecting the deterministic component of a linear system. The first is an extension of the dummy satura
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Eghbalian, Amirmohammad. "Data mining techniques for modeling the operating behaviors of smart building control valve systems." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20102.

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Background. One of the challenges about smart control valves system is processing and analyzing sensors data to extract useful information. These types of information can be used to detect the deviating behaviors which can be an indication of faults and issues in the system. Outlier detection is a process in which we try to find these deviating behaviors that occur in the system.Objectives. First, perform a literature review to get an insight about the machine learning (ML) and data mining (DM) techniques that can be applied to extract patternfrom time-series data. Next, model the operating be
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Slávik, Ľuboš. "Dynamická faktorová analýza časových řad." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445469.

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Táto diplomová práca sa zaoberá novým prístupom k zhlukovaniu časových rád na základe dynamického faktorového modelu. Dynamický faktorový model je technika redukujúca dimenziu a rozširuje klasickú faktorovú analýzu o požiadavku autokorelačnej štruktúry latentných faktorov. Parametre modelu sa odhadujú pomocou EM algoritmu za použitia Kalmanovho filtra a vyhladzovača a taktiež sú aplikované nevyhnutné podmienky na model, aby sa stal identifikovateľným. Po tom, ako je v práci predstavený teoretický koncept prístupu, dynamický faktorový model je aplikovaný na skutočné pozorované časové rady a prá
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Allab, Nedjmeddine. "Détection d'anomalies et de ruptures dans les séries temporelles. Applications à la gestion de production de l'électricité." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066658.

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Continental est l'outil de référence utilisé par EDF pour la gestion d'électricité à long terme. il permet d'élaborer la stratégie d'exploitation du parc constitué de centrales réparties sur toute l'europe. l'outil simule sur chaque zone et chaque scénario plusieurs variables telles que la demande d'électricité, la quantité générée ainsi que les coûts associés. nos travaux de thèse ont pour objectif de fournir des méthodes d'analyse de ces données de production afin de faciliter leur étude et leur synthèse. nous récoltons un ensemble de problématiques auprès des utilisateurs de continental que
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Ribeiro, Joana Patrícia Bordonhos. "Outlier identification in multivariate time series." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22200.

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Mestrado em Matemática e Aplicações<br>Com o desenvolvimento tecnológico, existe uma cada vez maior disponibilidade de dados. Geralmente representativos de situações do dia-a-dia, a existência de grandes quantidades de informação tem o seu interesse quando permite a extração de valor para o mercado. Além disso, surge importância em analisar não só os valores disponíveis mas também a sua associação com o tempo. A existência de valores anormais é inevitável. Geralmente denotados como outliers, a procura por estes valores é realizada comummente com o intuito de fazer a sua exclusão do est
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Åkerström, Emelie. "Real-time Outlier Detection using Unbounded Data Streaming and Machine Learning." Thesis, Luleå tekniska universitet, Datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80044.

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Accelerated advancements in technology, the Internet of Things, and cloud computing have spurred an emergence of unstructured data that is contributing to rapid growth in data volumes. No human can manage to keep up with monitoring and analyzing these unbounded data streams and thus predictive and analytic tools are needed. By leveraging machine learning this data can be converted into insights which are enabling datadriven decisions that can drastically accelerate innovation, improve user experience, and drive operational efficiency. The purpose of this thesis is to design and implement a sys
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Dao, Quang Hoan <1992&gt. "Anomaly detection with time series forecasting." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17320.

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Anomaly detection time series is a very large and complex field. In recent years, several techniques based on data science were designed in order to improve the efficiency of methods developed for this purpose. In this paper, we introduce Recurrent Neural Networks (RNNs) with LSTM units, ARIMA and Facebook Prophet library for dectecting the anomalies with time series forcasting. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. Unsupervised anomaly detection is the process of finding outlying records in a given dataset without prior need for tra
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Audibert, Julien. "Unsupervised anomaly detection in time-series." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS358.

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La détection d'anomalies dans les séries temporelles multivariées est un enjeu majeur dans de nombreux domaines. La complexité croissante des systèmes et l'explosion de la quantité de données ont rendu son automatisation indispensable. Cette thèse propose une méthode non supervisée de détection d'anomalies dans les séries temporelles multivariées appelée USAD. Cependant, les méthodes de réseaux de neurones profonds souffrent d'une limitation dans leur capacité à extraire des caractéristiques des données puisqu'elles ne s'appuient que sur des informations locales. Afin d'améliorer les performan
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Books on the topic "Time series outlier detection"

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B, Hudak Gregory, ed. Forecasting and time series analysis using the SCA statistical system: Vol. I : Box-Jenkins ARIMA modeling, intervention analysis, transfer function modeling, outlier detection and adjustment, exponential smoothing, related univariate methods. Scientific computing associates corp., 1992.

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Ogasawara, Eduardo, Rebecca Salles, Fabio Porto, and Esther Pacitti. Event Detection in Time Series. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75941-3.

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Cédric, Demeure, ed. Statistical signal processing: Detection, estimation, and time series analysis. Addison-Wesley Pub. Co., 1991.

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Riazuddin, Riaz. Detection and forecasting of Islamic calendar effects in Time Series Data. State Bank of Pakistan, 2002.

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W, Kundzewicz Zbigniew, Unesco, World Meteorological Organization, and World Climate Programme, eds. Detection of change in world-wide hydrological time series of maximum annual flow. World Meteorological Organization, 2004.

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Hlawatsch, F. Time-Frequency Analysis and Synthesis of Linear Signal Spaces: Time-Frequency Filters, Signal Detection and Estimation, and Range-Doppler Estimation. Springer US, 1998.

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Radzeijewski, Maciej. Development, use and application of the Hydrospect data analysis system for the detection of changes in hydrological time series for use in WCP-water and national hydrological services. World Meteorological Organization, 2004.

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Panova, Anna. Tourism statistics. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1046178.

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Contains a detailed overview of the basic concepts of the General theory of statistics, groups of statistics, absolute, relative and average values, statistical study of the relationship of socio-economic phenomena, time series and methods for the detection of trend in time series, indices and their use in tourism. The theoretical material is illustrated with examples from tourism and hospitality. Detail the history of the development, the subject and objectives, the indicator system of tourism statistics.&#x0D; Meets the requirements of Federal state educational standards of higher education
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An Influence method for outlier detection applied to time series traffic data. Institute for Transport Studies, University of Leeds, 1992.

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Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics). Wiley-Interscience, 2003.

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Book chapters on the topic "Time series outlier detection"

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Aggarwal, Charu C. "Time Series and Multidimensional Streaming Outlier Detection." In Outlier Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_9.

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Aggarwal, Charu C. "Time Series and Multidimensional Streaming Outlier Detection." In Outlier Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6396-2_8.

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Gupta, Manish, Jing Gao, Charu Aggarwal, and Jiawei Han. "Outlier Detection for Time Series and Data Sequences." In Outlier Detection for Temporal Data. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01905-0_2.

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Wang, Xiaochun, Xiali Wang, and Mitch Wilkes. "Unsupervised Fraud Detection in Environmental Time Series Data." In New Developments in Unsupervised Outlier Detection. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9519-6_10.

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Landauer, Max, Markus Wurzenberger, Florian Skopik, Giuseppe Settanni, and Peter Filzmoser. "Time Series Analysis: Unsupervised Anomaly Detection Beyond Outlier Detection." In Information Security Practice and Experience. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99807-7_2.

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Gołaszewski, Grzegorz. "Similarity-Based Outlier Detection in Multiple Time Series." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18058-4_10.

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Karioti, Vassiliki, and Polychronis Economou. "Detection of Outlier in Time Series Count Data." In Contributions to Statistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55789-2_15.

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Kalinke, Florian, Edouard Fouché, Haiko Thiessen, and Klemens Böhm. "Multi-kernel Times Series Outlier Detection." In Discovery Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_46.

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Wen, Junzhi, Md Reazul Islam, Azim Ahmadzadeh, and Rafal A. Angryk. "Improving Solar Flare Prediction by Time Series Outlier Detection." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23480-4_13.

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van de Wiel, L., D. M. van Es, and A. J. Feelders. "Real-Time Outlier Detection in Time Series Data of Water Sensors." In Advanced Analytics and Learning on Temporal Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65742-0_11.

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Conference papers on the topic "Time series outlier detection"

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Dutta, Soumili, and Ashutosh Kumar Jha. "Identifying Land Cover Change Via MAD-Based Outlier Detection in NDVI Time Series." In 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE, 2024. https://doi.org/10.1109/ingarss61818.2024.10984255.

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PK, Mithun Kumar, Mani Rupak Gurram, Al Amin Hossain, and Fathi Amsaad. "ARIMA-DCGAN Synergy: A Novel Adversarial Approach to Outlier Detection in Time Series Data." In NAECON 2024 - IEEE National Aerospace and Electronics Conference. IEEE, 2024. http://dx.doi.org/10.1109/naecon61878.2024.10670660.

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Kaufman, Tünde, and Jozsef Katona. "Time-Series Anomaly Detection of Mozi Malware in IoT Devices Using Arima and Local Outlier Factor." In 2025 IEEE 19th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2025. https://doi.org/10.1109/saci66288.2025.11030083.

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Shen, Weiguo, Tianhao Xia, Dongwei Xu, Qi Xuan, Yun Lin, and Wei Wang. "Fast Informer-Based Time Series Detection." In 2024 14th International Symposium on Antennas, Propagation and EM Theory (ISAPE). IEEE, 2024. https://doi.org/10.1109/isape62431.2024.10840860.

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Akouemo, Hermine N., and Richard J. Povinelli. "Time series outlier detection and imputation." In 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6939802.

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Zwilling, Chris E., and Michelle Yongmei Wang. "Multivariate voronoi outlier detection for time series." In 2014 Health Innovations and POCT. IEEE, 2014. http://dx.doi.org/10.1109/hic.2014.7038934.

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Ferdousi, Z., and A. Maeda. "Unsupervised Outlier Detection in Time Series Data." In 22nd International Conference on Data Engineering Workshops (ICDEW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdew.2006.157.

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Kieu, Tung, Bin Yang, Chenjuan Guo, and Christian S. Jensen. "Outlier Detection for Time Series with Recurrent Autoencoder Ensembles." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/378.

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We propose two solutions to outlier detection in time series based on recurrent autoencoder ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent neural networks (S-RNNs). Such networks make it possible to generate multiple autoencoders with different neural network connection structures. The two solutions are ensemble frameworks, specifically an independent framework and a shared framework, both of which combine multiple S-RNN based autoencoders to enable outlier detection. This ensemble-based approach aims to reduce the effects of some autoencoders being over
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Wang, Xin. "Two-phase outlier detection in multivariate time series." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019794.

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Mukhaiyar, Utriweni, Debby Masteriana, and Mila Isti Riani. "The outlier detection in time series regression model." In THE 8TH INTERNATIONAL CONFERENCE AND WORKSHOP ON BASIC AND APPLIED SCIENCE (ICOWOBAS) 2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0104584.

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Reports on the topic "Time series outlier detection"

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Grosskopf, Michael John. Aligning Time Series for Cyber-Physical Network Intrusion Detection. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1212612.

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Hall, Carole, James Tucker, and Drew Yarger. Elastic Changepoint Detection for Globally-indexed Functional Time Series Data with Climate Applications. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2462937.

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Chen, Z., and S. E. Grasby. Detection of decadal and interdecadal oscillations and temporal trend analysis of climate and hydrological time series, Canadian Prairies. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2009. http://dx.doi.org/10.4095/248138.

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Rose-Pehrsson, Susan, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, and Mark T. Wright. Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada383627.

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Deschamps, Henschel, and Robert. PR-420-123712-R01 Lateral Ground Movement Detection Capabilities Derived from Synthetic Aperture Radar. Pipeline Research Council International, Inc. (PRCI), 2014. http://dx.doi.org/10.55274/r0010831.

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The objective of this research was to quantify long-term ground deformation at the Belridge Oil Field, in the San Joaquin Valley (SJV), California using operational Interferometric Synthetic Aperture Radar (InSAR) monitoring techniques. A high spatial and temporal resolution, millimeter-precision time-series of ground deformation measurements was produced for the entire oil field from 2000 to 2012 using imagery from multiple satellites and beam modes. Trihedral Corner Reflectors (CRs) with co-located Global Navigation Satellite System (GNSS) units were used to validate the wide-area measuremen
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Huntley, D., D. Rotheram-Clarke, R. Cocking, J. Joseph, and P. Bobrowsky. Current research on slow-moving landslides in the Thompson River valley, British Columbia (IMOU 5170 annual report). Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331175.

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Interdepartmental Memorandum of Understanding (IMOU) 5170 between Natural Resources Canada (NRCAN), the Geological Survey of Canada (GSC) and Transport Canada Innovation Centre (TC-IC) aims to gain new insight into slow-moving landslides, and the influence of climate change, through testing conventional and emerging monitoring technologies. IMOU 5107 focuses on strategically important sections of the national railway network in the Thompson River valley, British Columbia (BC), and the Assiniboine River valley along the borders of Manitoba (MN) and Saskatchewan (SK). Results of this research ar
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Hailiang, Zhang, Wang Fuxiang, Sha Shengyi, Dai Lianshuang, Xuan Wenbo, and Ren Zhong. PR-469-173823-R01 In Line Inspection and Evaluation of Pinholes in Oil and Gas Pipelines. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011604.

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Pinhole leaks have been reported as a significant cause of oil and gas pipeline failures in recent years. From 2010 to 2015, at least 131 significant incidents involving oil and gas pipelines in the United States (101 and 30, respectively) were attributed to pinhole leaks. The 9th European Gas Pipeline Incident Data Group Report states that as of 2013, the five-year moving average failure frequency for pinholes was equal to an approximate annual rate of 0.105 failures per 1,000 kilometers of pipeline(1). Pinholes may result from normal pipeline corrosion during routine operations, such as micr
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Iselin, Columbus O'Donnell. Summary of bathythermograph observations from the western North Atlantic : October 1940 - December 1941. Woods Hole Oceanographic Institution, 2022. http://dx.doi.org/10.1575/1912/29563.

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The range of submarine detection is frequently limited by the refraction produced by vertical temperature gradients in the superficial layers of the ocean. In order to measure these temperature gradients and thus to permit predictions of the range, the bathythermograph was developed and is now being used on a considerable number of anti-submarine vessels, while a somewhat modified version of the instrument is being tried out on submarines. Some 6675 bathythermograph observations from the western North Atlantic have been examined in order to determine how frequently such observations should be
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t
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Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-ba
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