flying-insect-detection-and-classification-with-inexpensive-sensors

Flying Insect Detection and Classification with Inexpensive Sensors

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow ...

University of California, Riverside

In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence), unlike most anomaly detection algorithms that typically require many parameters. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is three to four orders of magnitude faster than brute force, while guaranteed to produce identical results. We evaluate our work with a comprehensive set of experiments on electrocardiograms and other medical datasets.

Finding unusual medical time-series subsequences: algorithms and applications.

Study of the molecular control of organ growth requires establishment of the causal relationship between gene expression and cell behaviors. We seek to understand this relationship at the shoot apical meristem (SAM) of model plant Arabidopsis thaliana. This requires the spatial mapping and temporal alignment of different functional domains into a single template. Live cell imaging techniques allow us to observe real time organ primordia growth and gene expression dynamics at cellular resolution. In this paper, we propose a framework for measurement of growth features at the 3D reconstructed surface of organ primordia, as well as an algorithm for robust time alignment of primordia. We computed areas and deformation values from reconstructed 3D surfaces of individual primordia from live cell imaging data. Based on these growth measurements, we applied a multiple features landscape matching algorithm (LAM-M), to ensure a reliable temporal alignment of multiple primordia. We also present an alternate parameter free growth alignment algorithm which performs as well as LAM-M for high quality data, but is more robust to the presence of outliers or noise. Results on primordia and guppy evolutionary growth data show that the proposed alignment algorithm is significantly better in the case of increased noise.

Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal alignment.

Study of the molecular control of organ growth requires establishment of the causal relationship between gene expression and cell behaviors. We seek to understand this relationship at the shoot apical meristem (SAM) of model plant Arabidopsis thaliana. This requires the spatial mapping and temporal alignment of different functional domains into a single template. Live-cell imaging techniques allow us to observe real-time organ primordia growth and gene expression dynamics at cellular resolution. In this paper, we propose a framework for the measurement of growth features at the 3D reconstructed surface of organ primordia, as well as algorithms for robust time alignment of primordia. We computed areas and deformation values from reconstructed 3D surfaces of individual primordia from live-cell imaging data. Based on these growth measurements, we applied a multiple feature landscape matching (LAM-M) algorithm to ensure a reliable temporal alignment of multiple primordia. Although the original landscape matching (LAM) algorithm motivated our alignment approach, it sometimes fails to properly align growth curves in the presence of high noise/distortion. To overcome this shortcoming, we modified the cost function to consider the landscape of the corresponding growth features. We also present an alternate parameter-free growth alignment algorithm which performs as well as LAM-M for high-quality data, but is more robust to the presence of outliers or noise. Results on primordia and guppy evolutionary growth data show that the proposed alignment framework performs at least as well as the LAM algorithm in the general case, and significantly better in the case of increased noise.

Eamonn Keogh

Department of Computer Science and Engineering

University of California, Riverside

Flying Insect Detection and Classification with Inexpensive Sensors

Eamonn Keogh

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Computer Science and Engineering

University of California, Riverside

Flying Insect Detection and Classification with Inexpensive Sensors

Department of Computer Science and Engineering