Most of our work has resulted in scholarly publications. On this page you can review our publications to get an idea about our work.
CLEAR-REF: Reproducibility Package for Reference-Dependent Interpretation of Clear-Sky Sampling Contrasts in Landsat-Based Agricultural Water-State Inference
September, 2026 • Dataset
Lu, Yeying, TANG, ZIJUN
This repository contains the reproducibility package for the manuscript:
"Reference-dependent interpretation of clear-sky sampling contrasts in Landsat-based agricultural water-state inference."
The p…
This repository contains the reproducibility package for the manuscript:
"Reference-dependent interpretation of clear-sky sampling contrasts in Landsat-based agricultural water-state inference."
The package provides analysis code, derived data products, metadata, and supporting materials required to reproduce the reported analyses of clear-sky observation contrasts under multiple environmental reference representations.
The study evaluates Landsat-based agricultural water-state inference using three complementary reference representations: modeled soil water, atmospheric demand, and station-based soil wetness. Observation-state uncertainty is explicitly retained and propagated without imputation or missing-at-random assumptions.
The repository includes processing scripts, derived analysis tables, metadata descriptions, and figure-generation resources. Original third-party datasets (including Landsat, NLDAS, gridMET, and USCRN source products) are not redistributed where restricted by provider policies; corresponding sources and retrieval information are documented in the metadata.
This repository supports reproducibility and transparency of the associated manuscript and does not represent a replacement for the original data providers.
Student perspectives on designing generative AI-based learning companions for higher education in Oman
June, 2026 • Journal article • International Journal of Evaluation and Research in Education (IJERE)
Samad, Saleem Raja Abdul, Ganesan, Pradeepa, Pria, Shanmuga, Radhakrishnan, Madhubala, Al Isaei, Khadija Ahmed
Artificial intelligence (AI) has rapidly permeated nearly every sector, from healthcare and finance to manufacturing, transportation, and communication. The emergence of generative AI (GenAI) applicat…
Artificial intelligence (AI) has rapidly permeated nearly every sector, from healthcare and finance to manufacturing, transportation, and communication. The emergence of generative AI (GenAI) applications, including intelligent assistants, recommendation engines, and predictive analytics, has further accelerated this transformation. Within education, these advances are reshaping teaching and learning, moving away from traditional instructor-centered models toward learner-centered ecosystems that emphasize adaptability, inclusivity, and self-directed growth. Despite the growing interest in AI-supported learning, existing literature reveals several important gaps that limit the effective integration of AI in educational contexts especially, students’ learning behaviors, and preferred learning resources, have received limited attention. This study explores student’s perceptions of AI-based educational tools to inform the design of an effective AI assistive learning companion for higher education. A structured survey was administered to 135 students across IT and business programs, examining demographics, language skills, learning habits, AI tool exposure, perceptions, concerns, and self-assessed learning confidence. The result of study highlights the need for AI learning companions that are adaptive, language-aware, discipline-specific, and ethically responsible, providing personalized scaffolding, practical skill reinforcement, and support for self-directed learning. These insights inform the design of AI tools that enhance learning confidence, inclusivity, and effectiveness in higher education.
AI assisted learningArtificial intelligenceDesign implicationsLearning behaviorLearning companion
INForum 2019 — Atas do 11º Simpósio de Informática
September, 2019 • Conference proceeding
Lourenço, João, Afonso, Ana Paula
Este volume contém as atas da 11a edição do Simpósio de Informática, INForum 2019, que decorreu no Campus de Azurém em Guimarães da Universid…
Este volume contém as atas da 11a edição do Simpósio de Informática, INForum 2019, que decorreu no Campus de Azurém em Guimarães da Universidade do Minho, Guimarães, nos dias 5 e 6 de setembro de 2019.
Reunindo a comunidade nacional, o INForum é um local privilegiado para a divulgação, discussão e reconhecimento de trabalhos científicos e avanços tecnológicos em Informática. O INForum oferece assim um palco especializado para promover, por um lado, o intercâmbio de conhecimento e experiência entre a academia e a indústria e, por outro lado, a estreia de jovens investigadores que procuram a divulgação, a crítica construtiva e o encorajamento ao seu trabalho.
Rehabilitation Engineering Explained: Assistive Technology & Human Recovery Systems
September, 2026 • Lesson
Prep4Uni.Online
This open educational resource (OER) curriculum module delivers a comprehensive, mathematically grounded foundation in Rehabilitation Engineering, detailing technologies that restore, augment, and sup…
This open educational resource (OER) curriculum module delivers a comprehensive, mathematically grounded foundation in Rehabilitation Engineering, detailing technologies that restore, augment, and support physical mobility and cognitive independence for individuals with neuromuscular and musculoskeletal impairments.
The module provides rigorous treatment of modern assistive systems, including powered lower-limb exoskeletons, myoelectric upper-limb prosthetics, carbon-fiber energy-storage-and-return (ESR) orthoses, smart wheelchair mechanics, and augmentative and alternative communication (AAC) gaze/neural decoders. Contemporary 2026 computational paradigms are integrated, highlighting:- Physics-Informed Neural Networks (PINNs) embedding Euler-Lagrange equations and Hill-type muscle contraction dynamics for non-invasive joint torque and muscle force estimation.- Invariant Extended Kalman Filtering (IEKF) formulated on the Lie group SE(3) to preserve kinematic invariants during multi-sensor inertial fusion.- Deep reinforcement learning and real-time admittance modulation for zero-impedance exoskeleton transparency.- A multi-variable systems engineering trade-off matrix analyzing actuator reflected inertia, prosthetic socket interface pressure limits (capillary ischemia threshold at 32 mmHg), and BCI sensor invasiveness.- An interactive kinematic gait simulator modeling real-time knee flexion angle compensation under variable muscle deficit, motor assist torque, and stride cadence.- Three tiers of self-assessment modules covering foundational principles, clinical scenario evaluations, and quantitative biomechanical calculations with step-by-step solutions.
Permanent web resource: https://prep4uni.online/stem/physical-technologies/biomedical-engineering/rehabilitation-engineering/
Rehabilitation EngineeringAssistive TechnologyPowered ExoskeletonsBiomechanicsProsthetics and Orthotics
There is an increasing interest in upgrading the EModel, a parametric tool for speech quality estimation, to the wideband and super-wideband contexts. The
Contemporary models of Unmanned Aerial Vehicles (UAVs) are largely developed using simulators. In a typical scheme, a flight simulator is dovetailed with a
Undertaking engineering research can be compounding for beginning graduate students and thwarting even for seasoned researchers. With a wealth of academic
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