Esta es nuestra última selección de publicaciones y patentes mundiales en inglés sobre Prompt Engineering, entre muchas revistas científicas en línea, clasificadas y centradas en prompt engineering, prompt design, contextualization, prompt variability, hyperparameter tuning, bias mitigation, zero-shot learning y few-shot learning.
Optimization of Deep Learning Models for Enhanced Respiratory Signal Estimation Using Wearable Sensors
Published on 2025-03-04 by Jiseon Kim, Jooyong Kim @MDPI
Abstract: Measuring breathing changes during exercise is crucial for healthcare applications. This study used wearable capacitive sensors to capture abdominal motion and extract breathing patterns. Data preprocessing methods included filtering and normalization, followed by feature extraction for classification. Despite the growing interest in respiratory monitoring, research on a deep learning-based analysis of breathing data remains limited. To address this research gap, we optimized CNN and ResNet thro[...]
Our summary: Study optimized CNN and ResNet for enhanced respiratory signal estimation using wearable sensors. Data preprocessing included filtering, normalization, and feature extraction for classification. Optimized ResNet outperformed CNN in accuracy and precision, demonstrating its capability for real-time assessment in medical applications.
Deep Learning Models, Optimization, Wearable Sensors, Respiratory Signal Estimation
Publication
Disease Infection Classification in Coconut Tree Based on an Enhanced Visual Geometry Group Model
Published on 2025-02-27 by Xiaocun Huang, Mustafa Muwafak Alobaedy, Yousef Fazea, S. B. Goyal, Zilong Deng @MDPI
Abstract: The coconut is a perennial, evergreen tree in the palm family that belongs to the monocotyledonous group. The coconut plant holds significant economic value due to the diverse functions served by each of its components. Any ailment that impacts the productivity of the coconut plantation will ultimately have repercussions on the associated industries and the sustenance of the families reliant on the coconut economy. Deep learning has the potential to significantly alter the landscape of plant dis[...]
Our summary: Disease detection in coconut trees using an EVGG16 model trained through transfer learning achieves high accuracy rates, revolutionizing plant disease detection and improving productivity in coconut plantations.
Deep Learning, Convolutional Neural Networks, Enhanced Visual Geometry Group Model, Disease Infection Classification
Publication
Applying Existing Large Language Models for Print Circuit Board Routing
Published on 2025-02-18 by Kangkang Zhang, Huailong Zhang, Aobo Li, Zhiping Yang, Xiuqin Chu @MDPI
Abstract: Large language models (LLMs), such as GPT-4.0 and Gemini, have achieved excellent performance on natural-language tasks, and they also show high expectations for logical reasoning. In the realm of print circuit board (PCB) routing, complex routing scenarios still rely on manual routing performed by seasoned engineers, which consumes significant human resources and time. This paper proposes an approach using few-shot and chain-of-thought training LLMs to tackle this issue, enabling LLMs to assist[...]
Our summary: Applying LLMs for PCB routing using few-shot and chain-of-thought training to reduce manual burden and improve design efficiency. Testing LLM performance in different routing scenarios and exploring fine-tuning techniques for enhanced effectiveness. Employing code synthetic cases to improve model capability in managing intricate tasks.
Large Language Models, PCB Routing, Few-shot Learning, Fine-tuning
Publication
Using Augmented Reality to Improve Touristic Efficacy
Published on 2025-02-18 by Miguel Cabeleira, Carlos Vaz de Carvalho @MDPI
Abstract: The tourism sector, a major economic contributor, seeks innovative approaches to enhance the user experience. In this evolving landscape of global tourism, using augmented reality (AR) technology can be a way to increase the engagement of tourists, but current AR applications often overwhelm (and bore) the users with excessive information. This study addresses the challenge of designing an AR solution that increases the efficiency of exploring and navigating tourist routes, while minimizing info[...]
Our summary: Using AR technology to enhance tourist routes by providing personalized and contextualized information, improving efficiency and engagement, tested in Porto and Chaves, Portugal.
Augmented Reality, Tourism, Efficiency, User Experience
Publication
Case Studies from Real-World Flight Operations
Published on 2025-02-17 by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri @MDPI
Abstract: This paper provides a comparative study of unsupervised machine learning (ML) methods for anomaly detection in flight data monitoring (FDM). The study applies various unsupervised ML techniques to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in industry. The results are validated by the industrial experts. The study finds that a hybrid Local Outlier Factor (LOF) approach provides significant advantages compared to the cur[...]
Our summary: Comparative study of unsupervised ML methods for anomaly detection in real-world flight data monitoring, validation by industrial experts, advantages of hybrid LOF approach compared to current techniques.
machine learning, anomaly detection, flight data monitoring, hybrid Local Outlier Factor
Publication
What Have Urban Digital Twins Contributed to Urban Planning and Decision Making? From a Systematic Literature Review Toward a Socio-Technical Research and Development Agenda
Published on 2025-02-13 by Shervin Azadi, Dena Kasraian, Pirouz Nourian, Pieter van Wesemael @MDPI
Abstract: Urban digital twins (UDTs) were first discussed in 2018. Seven years later, we ask: What has been their contribution to urban planning and decision making so far? Here, we systematically review 88 peer-reviewed articles to map and compare UDTs’ ambitions with their realized contributions. Our results indicate that despite the vast technical developments, socio-technical challenges have remained largely unaddressed, causing many of UDTs’ ambitions to remain unrealized.[...]
Our summary: Systematic literature review identifies challenges in realizing the ambitions of Urban Digital Twins for urban planning and decision making, proposing an Augmented Urban Planning agenda.
Urban Digital Twins, Urban Planning, Decision Making, Socio-Technical
Publication
Rendering techniques
Patent published on the 2024-11-16 in TW under Ref TW202446101 by FRAUNHOFER GES FORSCHUNG [DE] (Peters Nils [de], Silzle Andreas [de], Adami Alexander [de], Disch Sascha [de])
Abstract: There is disclosed renderer apparatus, comprising: a rendering unit configured to process an audio scene representation (402, 412) to be rendered and to receive at least one context-specific rule or parameter (441, 442), the rendering unit being configured to generate a rendered audio signal from the audio scene representation (402, 412) conditioned by the at least one context-specific rule or parameter (441, 442), a contextualization unit configured to receive and/or derive context-specific dat[...]
Our summary: Rendering unit processes audio scene representation and generates rendered audio signal based on context-specific rule, contextualization unit provides rule based on context-specific data.
Rendering techniques, renderer apparatus, audio scene representation, context-specific rule
Patent
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