IT/IST/IPLeiria Response to the Call for Evidence on JPEG Pleno Point Cloud Coding

This document proposes two scalable point cloud (PC) geometry codecs, submitted to the JPEG Call for Evidence on Point Cloud Coding (PCC).N/

Performance of Reference Evapotranspiration Estimation Methods at the Southern Paraná, Brazil

Knowing the atmospheric potential demand (ETo) implies in quantifying the ability of vegetated surfaces in absorbing water from the soil aiming at irrigation scheduling, crop yield prediction and water balance studies at a given site. Such information is extremely useful even under non-irrigated systems, for it makes possible to adjust sowing date within the crop growing season as a function of the local soil water availability, conditioning therefore a better reclamation of rainfall regime. Crop yield is significantly affected by the atmospheric conditions in order to galvanize researchers to scrutinize the regime of physical environment variables which directly interfere into the consumption of natural resources of crops in production fields. The aim of the current work was to investigate which of the atmospheric evaporative demand estimation methods are more suitable to depict physical reality of the water loss process in order to maximize crop yield and optimize irrigation scheduling under the climatic conditions of Southern Paraná, Brazil. The following methods were tested at two distinct sites of the studied region concerning its performance, taking into account the Penman-Monteith (FAO-56) approach as a standard reference for comparison purposes: simplified Penman, modified Bowen, Hargreaves-Samani, Camargo, and Linacre. The meteorological elements used for the calculation of ETo were monitored by an automatic weather station from Campbell Scientific Inc. throughout the years of 2008 through 2013. We concluded that empirical methods showed an unsatisfactory performance, whereas those methods that took into consideration net radiation as an input variable performed better, being the latter hence to be considered for agricultural planning and also for agrometeorological studies aiming at sustainability of the regional agriculture. The simplified Penman and modified Bowen methods were more accurate for estimating ETo in order to provide irrigation scheduling and indicate local soil water status at the region in study, because dismiss information on wind regimes that govern evapotranspiration rates

Performance of Different Models for Estimating the Global Solar Radiation in Brazil

Global solar irradiance (Qg) is an important variable of the physical environment that has been constantly used in agrometeorological models, either for climatic characterization or to give support to radiometric studies developed for irrigation planning and crop weather modeling approaches. The current study aimed to compare measured daily values of Qg with estimates of this variable by means of four different methods. For that throughout the period comprised between March 28th of 2008 and August 8th of 2011 at Ponta Grossa, PR, Brazil, a simple linear regression study confronting radiometric data measured by a pyranometer and estimates of Qg was proposed herein. Global transmittance was conditioned by atmospheric cloudiness. The models based on mean global transmittance in daily basis performed more satisfactorily and generated values of Qg with accuracy and exactness at the site in study, as confirmed by the statistical parameters employed to validate the USAge of models proposed by Angström-Prescott. However, the performance of the methodologies based on the determination of mean global transmittance under extreme atmospheric conditions, showed the highest Willmott coefficients, which was to be close to 1, reflecting then precision and reliability for the calculated values of Qg, when compared to observed values monitored at an automatic weather station

An efficient muscle fatigue model for forward and inverse dynamic analysis of human movements

AbstractThe aim of this work is to present the integration of a simple and yet efficient dynamic muscle fatigue model in a multibody formulation with natural coordinates. The fatigue model considers the force production history of each muscle to estimate its fitness level by means of a three-compartment theory approach. The model is easily adapted to co-operate with standard Hill-type muscle models, allowing the simulation and analysis of the redundant muscle forces generated in the presence of muscular fatigue. This has particular relevance in the design of orthotic devices to support human locomotion and manipulation

Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a undamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data

Deep Learning-Based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

info:eu-repo/semantics/publishedVersio

From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data

This database is accompanied by a folder with all the scripts used to process and handle the data described. It is openly hosted in Zenodo: https://doi.org/10.5281/zenodo.5801927Additionally, an extended code repository is available on Github (https://github.com/ManuelPalermo/HumanInertialPose.git) with updated code to not only process the data described, but also calculate kinematics, visualize and evaluate the resulting motions and offers extended support for general inertial pose estimation pipelines. All scripts are based on the Python programming language and, thus, open source. The code contains a permissive MIT license for unrestricted usage.Wearable technology is expanding for motion monitoring. However, open challenges still limit its widespread use, especially in low-cost systems. Most solutions are either expensive commercial products or lower performance ad-hoc systems. Moreover, few datasets are available for the development of complete and general solutions. This work presents 2 datasets, with low-cost and high-end Magnetic, Angular Rate, and Gravity(MARG) sensor data. Provides data for the complete inertial pose pipeline analysis, starting from raw data, sensor-to-segment calibration, multi-sensor fusion, skeleton-kinematics, to complete Human pose. Contains data from 21 and 10 participants, respectively, performing 6 types of sequences, presenting high variability and complex dynamics with almost complete range-of-motion. Amounts to 3.5 M samples, synchronized with a ground-truth inertial motion capture system. Presents a method to evaluate data quality. This database may contribute to develop novel algorithms for each pipeline's processing steps, with applications in inertial pose estimation algorithms, human movement forecasting, and motion assessment in industrial or rehabilitation settings. All data and code to process and analyze the complete pipeline is freely available.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n° 39479; Funding Reference: POCI-01-0247-FEDER-39479]. Sara Cerqueira was supported by the doctoral Grant SFRH/BD/151382/2021, financed by the Portuguese Foundation for Science and Technology (FCT), under MIT Portugal Program

Patients and family caregivers’ perceptions on two stress reduction interventions with patients with chronic diabetic foot ulcers: a qualitative study

M.Graça Pereira, Susana Pedras, André Louro et al. Patients and Family Caregivers’ Perceptions on Two Stress Reduction Interventions with Patients with Chronic Diabetic Foot Ulcers: A Qualitative Study, 05 August 2022, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-1910181/v1]Background The present study aimed to assess the perceptions of patients with chronic diabetic foot ulcers (DFUs) and their informal caregivers regarding the impact of two stress reduction interventions, such as a progressive muscle relaxation and hypnosis, on patients’ DFU and psychological wellbeing. Methods This multicenter study used a qualitative exploratory design and included individual interviews with eight patients with chronic DFUs and six family caregivers, using a semi-structured interview guide. Transcripts analysis employed thematic content analysis. Results Four key themes common to patients and their caregivers were found: 1) perspectives regarding the intervention; 2) intervention effectiveness; 3) the role of psychology on the DFU treatment; and 4) emotions and consequences associated with the DFU. Although themes were common to both treatment groups, sub-themes from the last two themes differed for patients that received muscle relaxation versus those who received hypnosis. One additional theme emerged from the caregiver’s interviews: 5) promotion of self care behaviors. Conclusion According to patients and caregivers, overall the two stress reduction interventions were beneficial for the DFU healing progression and emotional wellbeing. The hypnosis group also reported lasting effects. Participants suggested that psychological interventions such as stress reduction interventions could be included in the DFU standard treatment as an adjuvant to the clinical protocol for DFU treatment, preferably offered early on, when the patient begins treatment at the diabetic foot consultation

Stress reduction interventions for patients with chronic diabetic foot ulcers: a qualitative study into patients and caregivers' perceptions

The present study aimed to assess the perceptions of patients with chronic diabetic foot ulcers (DFUs) and their family caregivers regarding the impact of two stress reduction interventions on DFU and psychological wellbeing. The intervention included progressive muscle relaxation and hypnosis sessions.This study was conducted at the Psychology Research Centre (CIPsi/UM) School of Psychology, the University of Minho, supported by the Foundation for Science and Technology (FCT) through an FCT grant (PTDC/PSIGER/28163/2017) assigned to the frst author

Fully automated countrywide monitoring of fuel break maintenance operations

PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UIDB/00239/2020 UIDB/00066/2020Fuel break (FB) networks are strategic locations for fire control and suppression. In order to be effective for wildfire control, they need to be maintained through regular interventions to reduce fuel loads. In this paper, we describe a monitoring system relying on Earth observations to detect fuel reduction inside the FB network being implemented in Portugal. Two fast automated pixel-based methodologies for monthly monitoring of fuel removals in FB are developed and compared. The first method (M1) is a classical supervised classification using the difference and postdisturbance image of monthly image composites. To take into account the impact of different land cover and phenology in the detection of fuel treatments, a second method (M2) based on an innovative statistical change detection approach was developed. M2 explores time series of vegetation indices and does not require training data or user-defined thresholds. The two algorithms were applied to Sentinel-2 10 m bands and fully processed in the cloud-based platform Google Earth Engine. Overall, the unsupervised M2, which is based on a Welch t-test of two moving window averages, gives better results than the supervised M1 and is suitable for an automated countrywide fuel treatment detection. For both methods, two vegetation indices, the Modified Excess of Green and the Normalized Difference Vegetation Index, were compared and exhibited similar performances.publishersversionpublishe