Motivation in Language Learning. A qualitative study of teachers’ views on the importance of including pupils’ interests and real-life context in the teaching of English

This study investigates a sample of teachers’ experiences and opinions about including pupils’ interests and real-life context in the teaching of English. The intent of the study is to investigate the importance of choice of topics, teaching methods and learning context in order to enhance pupils’ motivation in language learning. The research question is as follows: To what extent do English teachers emphasize pupils’ interests and real-life context in the teaching of English, and how do they think that this affects pupils’ motivation?” To answer this research question, I have used a qualitative questionnaire to gain information about English teachers’ experiences and opinions related to this topic. The informants were selected on the basis that they had formal qualification and competence within the subject of English and are working or have worked as English teachers. The results indicate that the teachers do consider and promote pupils’ interests in their teaching. Furthermore, they try to relate the learning content to real-life contexts such as relevant news, media or happenings in local environment. Most of the informants experience a difference in pupils’ involvement and motivation according to which topic they are working with

Continuous Use of Biometric Sensor for Multiple Actions

This disclosure describes techniques to continuously sense the presence of an authenticated finger on a fingerprint sensor (or other biometric, such as a verified face in the field of view of a camera) of a device to enable multiple secure device actions. A user’s finger and fingerprint are sensed by the device sensor and authentication is performed for the user. The user remains authenticated while the finger maintains contact on the sensor, allowing the user to perform different secure actions without having to re-authenticate for each secure action that is performed while contact between the authenticated finger and the sensor is maintained. This technique can save time, reduce computational load and software complexity, and reduce user toil and annoyance of repeated fingerprint authentication. The described features also provide additional security due to continuous engagement of the fingerprint sensor by the user

Coordinated Listening by Multiple Voice Assistants in the Smart Home to Localize Smoke Detector Low-Battery Warning Chirp

This publication describes methods for coordinated listening by multiple voice assistant devices (e.g., smart home devices, smart speakers) to localize the sound of a low-battery warning chirp produced by a smoke detector. The voice assistant devices detect the sound of the low-battery warning chirp and measure the volume and timing of the chirp (generate “chirp information”). The chirp information can be provided to a processing unit of a computing device that estimates a location of the smoke detector and provides such information to a user

An Advanced Non-Gaussian Feature Space Method for POL-SAR Image Segmentation

This work extends upon our simple feature-based multichannel SAR segmentation method to incorporate highly desirable statistical properties into a computationally simple approach. The desirable properties include Markov random field contextual smoothing and goodness-of-fit testing to automatically obtain the significant number of classes. To achieve this we need to find an explicit class model to fit these non-Gaussian, non-symmetric or skewed feature space clusters. We take the skewed scale mixture of Gaussian scheme to model our classes and approximate it by a number of constrained Gaussians, thereby retaining much of the speed and simplicity of the original feature space method. The algorithm will be demonstrated on a real data and compared to an automatic Gaussian model

Aspects of model-based decompositions in radar polarimetry

Accepted manuscript version of the following article: Doulgeris, A.P. & Eltoft, T. (2015). Aspects of model-based decompositions in radar polarimetry. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/IGARSS.2015.7325746. Published version available at https://doi.org/10.1109/IGARSS.2015.7325746.In this paper, we further analyse the problem that polarimetric target decomposition methods in general have more physical parameters than equations, making the decomposition under-determined and hence have no unique solution. The common approach to get around this problem is to make certain assumptions, thus fixing one or more parameters, allowing the other free parameters to be solved from the set of expressions. We recently showed how to obtain additional information from fourth-order statistics to find a unique solution to model-based polarimetric decompositions ([1]). We previously showed a fourth-order unique solution that was valid only for Gaussian data, and indicated that non-Gaussian data led to an over-estimation in many of the parameters. This work describes our new method to obtain a generic textured data solution through an optimisation approach and presents preliminary results for a sea ice specific model

A wavelet domain filter for correlated speckle

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and the corresponding input data consist of features obtained from overlapping dual-pol Sentinel-1 (S1) data. Then, two, well-recognized ML methods are studied to learn the functional relationship between the output and input data. These ML approaches are the Gaussian process regression (GPR) and neural network (NN) for regression models. The goal is to use the aforementioned ML techniques to generate Arctic sea ice information from freely available dual-pol observations acquired by S1, which can, in general, only be generated from quad-pol data. Eight overlapping RS2 and S1 scenes were used to train and test the GPR and NN models. Statistical regression performance measures were computed to evaluate the strength of the ML regression methods. Then, two scenes were selected for further evaluation, where overlapping optical images were available as well. This allowed the visual interpretation of the maps estimated by the ML models. Finally, one of the methods was tested on an entire S1 scene to perform prediction on areas outside of the RS2 and S1 overlap. Our results indicate that the studied ML techniques can be utilized to increase the information retrieval capacity of the wide swath dual-pol S1 imagery while embedding physical properties in the methodology

Performance Analysis of Roll-Invariant PolSAR Parameters from C-band images with Regard to Sea Ice Type Separation

Source at https://www.vde-verlag.de/buecher/proceedings/.The Polarimetric Synthetic Aperture Radar (PolSAR) backscatter from a target is dependent on the incidence angle. Consequently, the associated roll invariant parameters are affected by changes in incidence angle. In this work, we identify a few of these parameters that remain robust in identifying sea ice features even under large incidence angle variations. We conclude that the helicity angle and the degree of purity are preferable over the scattering type angle in this respect. We utilize two overlapping RADARSAT-2 C-Band full polarimetric images, with a time difference of less than 2 hours, but with significant incidence angle difference

Deep Semisupervised Teacher-Student Model Based on Label Propagation for Sea Ice Classification

In this article, we propose a novelteacher–student-based label propagation deep semisupervised learning (TSLP-SSL) method for sea ice classification based on Sentinel-1 synthetic aperture radar data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from a limited number of labeled samples and a relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using a limited number of precisely labeled samples to achieve generalization capability by discovering the underlying sea ice characteristics also from unlabeled data. We perform experimental analysis considering a standard dataset consisting of properly labeled sea ice data spanning over different time slots of the year. Both qualitative and quantitative results obtained on this dataset show that our proposed TSLP-SSL method outperforms deep supervised and semisupervised reference methods

A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization

When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible and efficient method based on graph Laplacians for information selection at different levels of data fusion. The proposed approach combines data structure and information content to address the limitations of existing graph-Laplacian-based methods in dealing with heterogeneous datasets. Moreover, it adapts the selection to each homogenous area of the considered images according to their underlying properties. Experimental tests carried out on several multivariate remote sensing datasets show the consistency of the proposed approach