A Novel Type of Feature Extraction Algorithm Based on Multi-Directional Analysis for Visual Optical Electronic Nose

According to the fact that the response images of a visual optical electronic nose (E-nose) have a huge amount of data, various frequency components and complex periodic and directional information, a novel type of visual optical E-nose (VOE-nose) feature extraction algorithm based on multi-directional analysis by directional filter bank (DFB) was proposed in this paper. Firstly, the gas sensing model of the VOE-nose was introduced, and the basic principle of DFB algorithm for feature extraction was described. Second, response images of NO2 in different wavebands were collected by the VOE-nose platform. Third, typical feature extraction and DFB feature extraction algorithms were used to extract features of response data, then the feature dimension reduction and pattern recognition algorithms were used to analyze the features. The mean classification accuracy is more than 95%, which verifies the superiority of the DFB feature extraction algorithm.

Comparing Arctic Sea Ice Model Simulations to Satellite Observations by Multiscale Directional Analysis of Linear Kinematic Features

Sea ice models have become essential components of weather, climate, and ocean models. A realistic representation of sea ice affects the reliability of process representation, environmental forecast, and climate projections. Realistic simulations of sea ice kinematics require the consideration of both large-scale and finescale geomorphological structures such as linear kinematic features (LKF). We propose a multiscale directional analysis (MDA) that diagnoses the spatial characteristics of LKFs. The MDA is different from previous analyses in that it (i) does not detect LKFs as objects, (ii) takes into account the width of LKFs, and (iii) estimates scale-dependent orientation and intersection angles. The MDA is applied to pairs of deformation fields derived from satellite remote sensing data and from a numerical model simulation with a horizontal grid spacing of ~4.5 km. The orientation and intersection angles of LKFs agree with the observations and confirm the visual impression that the intersection angles tend to be smaller in the satellite data compared to the model data. The MDA distributions can be used to compare satellite data and numerical model fields using conventional metrics such as a Euclidean distance, the Bhattacharyya coefficient, or the Earth mover’s distance. The latter is found to be the most meaningful metric to compare distributions of LKF orientations and intersection angles. The MDA proposed here provides a tool to diagnose if modified sea ice rheologies lead to more realistic simulations of LKFs.

(Un)Managing Emotions at the Forefront: Stories from Shoreham Picket Line

In this article, I reflect on my experience as an active rank and file member of CUPE 3903, the union representing contract faculty and graduate students at York University in Toronto, Ontario, during the 2018 York University Strike, where I volunteered as a front-line communicator, or “car talker”. Drawing on these experiences, I reflect on the ways in which picketers generally try to (un)manage the emotions of drivers passing through the picket line. My analysis is focused on a particular venue - the Shoreham picket line located at the southwest entrance of the university, and centers around my personal interactions with the drivers crossing the picket line during the morning hours from March 2018 to May 2018. My analysis aims to open up space to discuss the largely overlooked role that the emotions of the public play in shaping the picket line experience. In particular, I provide a multi-directional analysis of the encounters that occurred between the picketers and the general public at the Shoreham picket line during the 2018 strike, highlighting the multiplicity of variables, such as the environment, the pre-existing beliefs of the participants, and expressions of collective anger, which informed these encounters. In doing this, I illuminate the complexity of the intertwined relationship between emotional and cognitive framing, thereby providing a more comprehensive model for understanding the role that emotions play in social movement organizing.

Comparing Arctic Sea Ice Model Simulations to Satellite observations by Multiscale Directional Analysis of Sea Ice Deformation

<p>Sea ice models have become essential components of weather, climate and ocean models. The reliability of process studies, environmental forecasts and climate projections alike depend on a realistic representation of sea ice. Developing and evaluating sea ice models requires methods for both large scales and fine-scale geomorphological structures such as linear kinematic features (LKF). We introduce a Multiscale Directional Analysis (MDA) method that diagnoses distributions of LKF orientation and intersection angles. The MDA method is different from previous methods in that it (a)  takes into account the width of LKFs instead of estimating the orientation of centerlines; (b) separates curve-like features from point-like features providing the opportunity to reach a unified definition of LKF in both numerical and observational fields; (c) estimates scale-dependent intersection angles.</p>

Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as “geolocation”) in the form of a tweet (also referred to as a “check-in”), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user’s activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user’s daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time.