Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Spherical Vision Transformer for 360-degree Video Saliency Prediction

The growing interest in omnidirectional videos (ODVs) that capture the full field-of-view (FOV) has gained 360-degree saliency prediction importance in computer vision. However, predicting where humans look in 360-degree scenes presents unique challenges, including spherical distortion, high resolution, and limited labelled data. We propose a novel vision-transformer-based model for omnidirectional videos named SalViT360 that leverages tangent image representations. We introduce a spherical geometry-aware spatiotemporal self-attention mechanism that is capable of effective omnidirectional video understanding. Furthermore, we present a consistency-based unsupervised regularization term for projection-based 360-degree dense-prediction models to reduce artefacts in the predictions that occur after inverse projection. Our approach is the first to employ tangent images for omnidirectional saliency prediction, and our experimental results on three ODV saliency datasets demonstrate its effectiveness compared to the state-of-the-art.Comment: 12 pages, 4 figures, accepted to BMVC 202

Evaluation of a Home Biomonitoring Autonomous Mobile Robot

Increasing population age demands more services in healthcare domain. It has been shown that mobile robots could be a potential solution to home biomonitoring for the elderly. Through our previous studies, a mobile robot system that is able to track a subject and identify his daily living activities has been developed. However, the system has not been tested in any home living scenarios. In this study we did a series of experiments to investigate the accuracy of activity recognition of the mobile robot in a home living scenario. The daily activities tested in the evaluation experiment include watching TV and sleeping. A dataset recorded by a distributed distance-measuring sensor network was used as a reference to the activity recognition results. It was shown that the accuracy is not consistent for all the activities; that is,mobile robot could achieve a high success rate in some activities but a poor success rate in others. It was found that the observation position of the mobile robot and subject surroundings have high impact on the accuracy of the activity recognition, due to the variability of the home living daily activities and their transitional process. The possibility of improvement of recognition accuracy has been shown too

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

Our research is focused on the development of an at-home health care biomonitoringmobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals.Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy

Uncovering the potentialities of protic ionic liquids based on alkanolammonium and carboxylate ions and their aqueous solutions as non-derivatizing solvents of Kraft lignin

The present study scrutinized in depth the ability of alkanolammonium-based Protic Ionic Liquids (PILs) with carboxylate anions to dissolve Kraft lignin at 323.15 K. A focus was put on understanding the role of both PIL ions and water on the dissolution process. The results demonstrated that the anion plays a more important role in lignin dissolution than the cation. Furthermore, lignin dissolution was favored by increasing the alkyl chain of the carboxylate anion, while a smaller cation with lower number of hydroxyalkyl groups performed better. Among the studied solvents, the 2-hydroxyethylammonium hexanoate (HEAH) displayed the highest lignin solubility (37 wt%). In general, the addition of water had a negative influence on lignin solubility with the tested PILs. A sharp decrease in lignin solubility curves of 2-hydroxyethylammonium formate (HEAF) and acetate (HEAA) was observed, while a more softly effect was observed for 2-hydroxyethylammonium propionate (HEAP) and HEAH with the addition of water. However, a distinct behavior was observed for 2-hydroxyethylammonium octanoate (HEAO) that acted as hydrotrope enhancing lignin solubility in aqueous solutions to a maximum value at 40 wt% water content. Furthermore, by increasing the temperature, the lignin solubility was favored due to endothermic behavior of lignin dissolution process. The dissolution of Kraft lignin was also performed at 393.15 K to unravel any lignin modification unleashed by PILs. GPC, FTIR-ATR and 2D NMR were employed for lignin characterization and the changes observed between native lignin and recovered lignin samples were negligible demonstrating the non-derivatizing char- acter of the PILs. Moreover, the recycle of 2-hydroxyethylammonium propionate (HEAP) was successfully de- monstrated for at least 3 cycles. In this way, PILs are herein revealed as promising solvents to apply in lignin valorization towards more efficient and eco-friendly processes.Suzano Papel & Celulosepublishe