Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102071
Andres Felipe Ruiz-Hurtado , Juliana Perez Bolaños , Darwin Alexis Arrechea-Castillo , Juan Andres Cardoso
Tree monitoring is a challenging task due to the labour-intensive and time-consuming data collection methods required. We present TreeEyed, a QGIS plugin designed to facilitate the monitoring of trees using remote sensing RGB imagery and artificial intelligence models. The plugin offers several tools including tree inference process for tree segmentation and detection. This tool was implemented to facilitate the manipulation and processing of Geographical Information System (GIS) data from different sources, allowing multi resolution, variable extent, and generating results in a standard GIS format (georeferenced raster and vector). Additional options like postprocessing, dataset generation, and data validation are also incorporated.
{"title":"TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models","authors":"Andres Felipe Ruiz-Hurtado , Juliana Perez Bolaños , Darwin Alexis Arrechea-Castillo , Juan Andres Cardoso","doi":"10.1016/j.softx.2025.102071","DOIUrl":"10.1016/j.softx.2025.102071","url":null,"abstract":"<div><div>Tree monitoring is a challenging task due to the labour-intensive and time-consuming data collection methods required. We present TreeEyed, a QGIS plugin designed to facilitate the monitoring of trees using remote sensing RGB imagery and artificial intelligence models. The plugin offers several tools including tree inference process for tree segmentation and detection. This tool was implemented to facilitate the manipulation and processing of Geographical Information System (GIS) data from different sources, allowing multi resolution, variable extent, and generating results in a standard GIS format (georeferenced raster and vector). Additional options like postprocessing, dataset generation, and data validation are also incorporated.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102071"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102016
Daniel Huici, Ricardo J. Rodríguez, Eduardo Mena
APOTHEOSIS is a tool for efficiently identifying and comparing data similarity in large datasets, addressing challenges faced by traditional methods such as scalability and speed. APOTHEOSIS overcomes them by combining advanced algorithms and data structures, enabling fast and accurate similarity analysis. Specifically, it uses a custom hierarchical small navigation world as an approximate -nearest neighbors search method, and approximate similarity digests algorithms to find common features between similar data items, also supporting various distance metrics beyond vector-based approaches. Our software tool is designed for seamless integration into research workflows, improving reproducibility and facilitating the comparison of large-scale, high-dimensional data comparison across multiple domains.
{"title":"APOTHEOSIS: An efficient approximate similarity search system","authors":"Daniel Huici, Ricardo J. Rodríguez, Eduardo Mena","doi":"10.1016/j.softx.2024.102016","DOIUrl":"10.1016/j.softx.2024.102016","url":null,"abstract":"<div><div><span>APOTHEOSIS</span> is a tool for efficiently identifying and comparing data similarity in large datasets, addressing challenges faced by traditional methods such as scalability and speed. <span>APOTHEOSIS</span> overcomes them by combining advanced algorithms and data structures, enabling fast and accurate similarity analysis. Specifically, it uses a custom hierarchical small navigation world as an approximate <span><math><mi>K</mi></math></span>-nearest neighbors search method, and approximate similarity digests algorithms to find common features between similar data items, also supporting various distance metrics beyond vector-based approaches. Our software tool is designed for seamless integration into research workflows, improving reproducibility and facilitating the comparison of large-scale, high-dimensional data comparison across multiple domains.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102016"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102024
José L. Garrido-Labrador, Jesús M. Maudes-Raedo, Juan J. Rodríguez, César I. García-Osorio
SSLearn is an open-source Python-based library that advances semi-supervised learning (SSL) with a focus on wrapper algorithms and restricted set classification (RSC), a novel paradigm. It fosters innovation by allowing researchers to modify methods or create new ones, facilitating access to state-of-the-art algorithms and comparative studies. As the only library incorporating RSC for constrained classification, SSLearn fills an important gap in SSL tools. Fully compatible with Scikit-Learn, it integrates seamlessly into research workflows, lowering the barrier to entry to SSL and catalyzing its adoption in diverse domains. This makes SSLearn a critical resource for advancing SSL research and applications.
{"title":"SSLearn: A Semi-Supervised Learning library for Python","authors":"José L. Garrido-Labrador, Jesús M. Maudes-Raedo, Juan J. Rodríguez, César I. García-Osorio","doi":"10.1016/j.softx.2024.102024","DOIUrl":"10.1016/j.softx.2024.102024","url":null,"abstract":"<div><div>SSLearn is an open-source Python-based library that advances semi-supervised learning (SSL) with a focus on wrapper algorithms and restricted set classification (RSC), a novel paradigm. It fosters innovation by allowing researchers to modify methods or create new ones, facilitating access to state-of-the-art algorithms and comparative studies. As the only library incorporating RSC for constrained classification, SSLearn fills an important gap in SSL tools. Fully compatible with Scikit-Learn, it integrates seamlessly into research workflows, lowering the barrier to entry to SSL and catalyzing its adoption in diverse domains. This makes SSLearn a critical resource for advancing SSL research and applications.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102024"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102002
Raphaël Gass , Zhongliang Li , Rachid Outbib , Samir Jemei , Daniel Hissel
The urgency of the energy transition requires improving the performance and longevity of hydrogen technologies. AlphaPEM is a dynamic one-dimensional (1D) physics-based PEM fuel cell system simulator, programmed in Python and experimentally validated. It offers a good balance between accuracy and execution speed. The modular architecture allows for addition of new features, and it has a user-friendly graphical interface. An automatic calibration method is proposed to match the model to the studied fuel cell. The software provides information on the internal states of the system in response to any current density and can produce polarization and EIS curves. AlphaPEM facilitates the use of a model in embedded conditions, allowing real-time modification of the fuel cell’s operating conditions.
{"title":"AlphaPEM: An open-source dynamic 1D physics-based PEM fuel cell model for embedded applications","authors":"Raphaël Gass , Zhongliang Li , Rachid Outbib , Samir Jemei , Daniel Hissel","doi":"10.1016/j.softx.2024.102002","DOIUrl":"10.1016/j.softx.2024.102002","url":null,"abstract":"<div><div>The urgency of the energy transition requires improving the performance and longevity of hydrogen technologies. AlphaPEM is a dynamic one-dimensional (1D) physics-based PEM fuel cell system simulator, programmed in Python and experimentally validated. It offers a good balance between accuracy and execution speed. The modular architecture allows for addition of new features, and it has a user-friendly graphical interface. An automatic calibration method is proposed to match the model to the studied fuel cell. The software provides information on the internal states of the system in response to any current density and can produce polarization and EIS curves. AlphaPEM facilitates the use of a model in embedded conditions, allowing real-time modification of the fuel cell’s operating conditions.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102002"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102020
Lucas A. Polson , Roberto Fedrigo , Chenguang Li , Maziar Sabouri , Obed Dzikunu , Shadab Ahamed , Nicolas Karakatsanis , Sara Kurkowska , Peyman Sheikhzadeh , Pedro Esquinas , Arman Rahmim , Carlos Uribe
There is a need for open-source libraries in emission tomography that (i) use modern and popular backend code to encourage community contributions and (ii) offer support for the multitude of reconstruction techniques available in recent literature, such as those that employ artificial intelligence. The purpose of this research was to create and evaluate a GPU-accelerated, open-source, and user-friendly image reconstruction library, designed to serve as a central platform for the development, validation, and deployment of various tomographic reconstruction algorithms. PyTomography was developed using Python and inherits the GPU-accelerated functionality of PyTorch and parallelproj for fast computations. Its flexible and modular design decouples system matrices, likelihoods, and reconstruction algorithms, simplifying the process of integrating new imaging modalities using various python tools. Example use cases demonstrate the software capabilities in parallel hole SPECT and listmode PET imaging. Overall, we have developed and publicly share PyTomography, a highly optimized and user-friendly software for medical image reconstruction, with a class hierarchy that fosters the development of novel imaging applications.
{"title":"PyTomography: A python library for medical image reconstruction","authors":"Lucas A. Polson , Roberto Fedrigo , Chenguang Li , Maziar Sabouri , Obed Dzikunu , Shadab Ahamed , Nicolas Karakatsanis , Sara Kurkowska , Peyman Sheikhzadeh , Pedro Esquinas , Arman Rahmim , Carlos Uribe","doi":"10.1016/j.softx.2024.102020","DOIUrl":"10.1016/j.softx.2024.102020","url":null,"abstract":"<div><div>There is a need for open-source libraries in emission tomography that (i) use modern and popular backend code to encourage community contributions and (ii) offer support for the multitude of reconstruction techniques available in recent literature, such as those that employ artificial intelligence. The purpose of this research was to create and evaluate a GPU-accelerated, open-source, and user-friendly image reconstruction library, designed to serve as a central platform for the development, validation, and deployment of various tomographic reconstruction algorithms. PyTomography was developed using Python and inherits the GPU-accelerated functionality of PyTorch and parallelproj for fast computations. Its flexible and modular design decouples system matrices, likelihoods, and reconstruction algorithms, simplifying the process of integrating new imaging modalities using various python tools. Example use cases demonstrate the software capabilities in parallel hole SPECT and listmode PET imaging. Overall, we have developed and publicly share PyTomography, a highly optimized and user-friendly software for medical image reconstruction, with a class hierarchy that fosters the development of novel imaging applications.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102020"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102014
Błażej Zieliński, Szymon Ściegienny, Hubert Orlicki, Wojciech Książek
The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implemented in many popular programming languages. However, the algorithm has continued to evolve, with newer, improved variants often achieving superior results over the original. Unfortunately, many of these modifications are not readily accessible as prebuilt programming solutions, creating a need for a comprehensive programming library that includes the most popular and effective variants of the base algorithm. The library we designed, DetPy (Differential Evolution Tools), provides implementations of the standard differential evolution algorithm along with 15 distinct variants. This tool allows researchers working on optimization problems to compare multiple algorithmic approaches, making it easier to select the most effective solution for their specific challenges.
{"title":"DetPy (Differential Evolution Tools): A Python toolbox for solving optimization problems using differential evolution","authors":"Błażej Zieliński, Szymon Ściegienny, Hubert Orlicki, Wojciech Książek","doi":"10.1016/j.softx.2024.102014","DOIUrl":"10.1016/j.softx.2024.102014","url":null,"abstract":"<div><div>The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implemented in many popular programming languages. However, the algorithm has continued to evolve, with newer, improved variants often achieving superior results over the original. Unfortunately, many of these modifications are not readily accessible as prebuilt programming solutions, creating a need for a comprehensive programming library that includes the most popular and effective variants of the base algorithm. The library we designed, DetPy (Differential Evolution Tools), provides implementations of the standard differential evolution algorithm along with 15 distinct variants. This tool allows researchers working on optimization problems to compare multiple algorithmic approaches, making it easier to select the most effective solution for their specific challenges.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102014"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102050
Margareta J. Hellmann, Bruno M. Moerschbacher, Stefan Cord-Landwehr
The composition and enzymatic cleavage of binary linear copolymers (LCPs) composed of two different units, such as the glycans chitosan, homogalacturonan, alginate, or hyaluronan, are widely investigated by researchers from various disciplines including biomedicine, material sciences, and biotechnology. The LCP Simulator is a user-friendly free web tool available to anyone without registration at https://lcp-simulator.anvil.app. The objective is to provide support for LCP-researchers, including those lacking experience in in silico analyses, by offering a low-threshold possibility to simulate a) the analysis of distributions of the two units within LCPs, and b) the influence of LCP properties on the composition of products after cleavage with enzymes of defined subsite specificities.
{"title":"LCP simulator: An easy-to-use web tool to simulate pattern analysis and enzymatic cleavage of binary linear copolymers","authors":"Margareta J. Hellmann, Bruno M. Moerschbacher, Stefan Cord-Landwehr","doi":"10.1016/j.softx.2025.102050","DOIUrl":"10.1016/j.softx.2025.102050","url":null,"abstract":"<div><div>The composition and enzymatic cleavage of binary linear copolymers (LCPs) composed of two different units, such as the glycans chitosan, homogalacturonan, alginate, or hyaluronan, are widely investigated by researchers from various disciplines including biomedicine, material sciences, and biotechnology. The LCP Simulator is a user-friendly free web tool available to anyone without registration at <span><span>https://lcp-simulator.anvil.app</span><svg><path></path></svg></span>. The objective is to provide support for LCP-researchers, including those lacking experience in <em>in silico</em> analyses, by offering a low-threshold possibility to simulate a) the analysis of distributions of the two units within LCPs, and b) the influence of LCP properties on the composition of products after cleavage with enzymes of defined subsite specificities.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102050"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102019
Alfonso José Zozaya Sahad
This manuscript introduces a synthetic aperture radar (SAR) simulator and presents the application of the Range-Doppler Algorithm (RDA) to process the simulated SAR data. The simulator efficiently generates raw data by considering various radar parameters and point targets within a specified probing area. The RDA is used to perform range focusing, range cell migration correction (RCMC), and azimuth focusing on the raw data. The resulting SAR image is presented as an outcome of this process, providing valuable insights into SAR system design and algorithm validation.
{"title":"SARrawSim: Synthetic Aperture Radar Raw Data Simulator","authors":"Alfonso José Zozaya Sahad","doi":"10.1016/j.softx.2024.102019","DOIUrl":"10.1016/j.softx.2024.102019","url":null,"abstract":"<div><div>This manuscript introduces a synthetic aperture radar (SAR) simulator and presents the application of the Range-Doppler Algorithm (RDA) to process the simulated SAR data. The simulator efficiently generates raw data by considering various radar parameters and point targets within a specified probing area. The RDA is used to perform range focusing, range cell migration correction (RCMC), and azimuth focusing on the raw data. The resulting SAR image is presented as an outcome of this process, providing valuable insights into SAR system design and algorithm validation.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102019"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号