SoftwareX最新文献

Version [2.0] — [pyMCMA: Uniformly distributed Pareto-front representation]

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-10 DOI: 10.1016/j.softx.2025.102097

Marek Makowski , Janusz Granat , Andrii Shekhovtsov , Zbigniew Nahorski , Jinyang Zhao

引用次数: 0

StepLogger and EvaalScore: The software suite of the IPIN onsite indoor localization competition

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-08 DOI: 10.1016/j.softx.2025.102115

Michele Girolami, Paolo Baronti, Francesco Potortì, Antonino Crivello, Filippo Palumbo

{"title":"StepLogger and EvaalScore: The software suite of the IPIN onsite indoor localization competition","authors":"Michele Girolami, Paolo Baronti, Francesco Potortì, Antonino Crivello, Filippo Palumbo","doi":"10.1016/j.softx.2025.102115","DOIUrl":"10.1016/j.softx.2025.102115","url":null,"abstract":"<div><div>This paper illustrates the software suite developed for Track 1 of the IPIN competition, which evaluates smartphone apps for indoor localization. Competitors have one day before the trial day to survey the competition area. On the trial day, an independent “actor” carries the competing system on smartphone and walks a predefined path. Competing systems provide continuous location estimates, which are later compared to a ground truth. We describe the software suite used to gather and present the results: the StepLogger Android application for real-time logging of position estimates and the EvaalScore tool for performance evaluation.</div><div>StepLogger collects location estimating data from competitors with a timestamp, while EvaalScore calculates the accuracy of the competing systems. The competition ranking is based on the third quartile of point localization error. The presented software suite ensures a standardized and fair assessment of competing systems, thus promoting reproducibility and transparency in indoor localization research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102115"},"PeriodicalIF":2.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577480","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}

引用次数: 0

Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-07 DOI: 10.1016/j.softx.2025.102122

Carlos Angarita , Carlos Montes , Orlando Arroyo

引用次数: 0

ars548_ros: An ARS 548 RDI radar driver for ROS

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-06 DOI: 10.1016/j.softx.2025.102111

Fernando Fernández-Calatayud , Lucía Coto , David Alejo , José Javier Carpio , Fernando Caballero , Luis Merino

{"title":"ars548_ros: An ARS 548 RDI radar driver for ROS","authors":"Fernando Fernández-Calatayud , Lucía Coto , David Alejo , José Javier Carpio , Fernando Caballero , Luis Merino","doi":"10.1016/j.softx.2025.102111","DOIUrl":"10.1016/j.softx.2025.102111","url":null,"abstract":"<div><div>The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long-range radar sensors with new RF antenna arrays, which offer digital beamforming. This radar measures independently the distance, speed, and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to the best of our knowledge, there are no open-source drivers available for Linux systems to enable users to analyze the data acquired by the sensor. In this paper, we present a driver that can interpret the data from the ARS 548 RDI sensor and make it available over the Robot Operating System versions 1 and 2 (ROS and ROS2). Thus, these data can be stored, represented, and analyzed using the powerful tools offered by ROS. Besides, our driver offers advanced object features provided by the sensor, such as relative estimated velocity and acceleration of each object, its orientation and angular velocity. We focus on the configuration of the sensor and the use of our driver including its filtering and representation tools. Besides, we offer a video tutorial to help in its configuration process. Finally, a dataset acquired with this sensor and an Ouster OS1-32 LiDAR sensor, to have baseline measurements, is available so that the user can check the correctness of our driver.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102111"},"PeriodicalIF":2.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549678","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}

引用次数: 0

gymfolio: A Reinforcement learning environment for Portfolio Optimization in Python

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-06 DOI: 10.1016/j.softx.2025.102106

Francisco Espiga-Fernández, Álvaro García-Sánchez, Joaquín Ordieres-Meré

引用次数: 0

“Version 1.4.0 – pyfao56: FAO-56 evapotranspiration in Python”

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-06 DOI: 10.1016/j.softx.2025.102109

Kelly R. Thorp , Dinesh Gulati , Meetpal Kukal , Reagan Ames , Tyler Pokoski , Kendall C. DeJonge

引用次数: 0

SegTrackDetect: A window-based framework for tiny object detection via semantic segmentation and tracking

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-03 DOI: 10.1016/j.softx.2025.102110

Aleksandra Kos , Karol Majek , Dominik Belter

{"title":"SegTrackDetect: A window-based framework for tiny object detection via semantic segmentation and tracking","authors":"Aleksandra Kos , Karol Majek , Dominik Belter","doi":"10.1016/j.softx.2025.102110","DOIUrl":"10.1016/j.softx.2025.102110","url":null,"abstract":"<div><div>This work introduces SegTrackDetect, an open-source, window-based framework for small and tiny object detection. Detecting tiny objects in high-resolution images is essential for real-time applications such as autonomous navigation and surveillance but is challenging due to the computational complexity of processing large images while maintaining the speed needed for timely decision-making. The proposed framework addresses this challenge by performing inference in selected regions only, significantly reducing the computational burden compared to standard sliding window methods. Thanks to full-resolution inference within these selected regions, lightweight detectors can be employed, further accelerating the process. The framework selects detection sub-windows based on Regions of Interest (ROIs) generated by the ROI Estimation and ROI Prediction modules. The ROI Estimation Module creates binary masks of ROIs from input images, while the ROI Prediction Module uses an object tracker to predict object locations in the current frame based on previous detections. Detections from multiple sub-windows are aggregated and filtered to eliminate redundancies, ensuring high-quality results. SegTrackDetect is optimized with inference speed in mind, offering a highly efficient pipeline while providing users with the flexibility to customize models. It supports a wide range of industrial and research applications by allowing users to adjust model parameters and incorporate new models with custom pre- and post-processing functions. It is compatible with both image and video data, automatically determining the data type from the dataset structure. SegTrackDetect is especially well-suited for tasks such as drone or satellite-based tiny object detection. The code is available at <span><span>https://github.com/deepdrivepl/SegTrackDetect</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102110"},"PeriodicalIF":2.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534987","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}

引用次数: 0

NeoCoMM: Neocortical neuro-inspired computational model for realistic microscale simulations

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-03 DOI: 10.1016/j.softx.2025.102108

Mariam Al Harrach , Maxime Yochum , Fabrice Wendling

{"title":"NeoCoMM: Neocortical neuro-inspired computational model for realistic microscale simulations","authors":"Mariam Al Harrach , Maxime Yochum , Fabrice Wendling","doi":"10.1016/j.softx.2025.102108","DOIUrl":"10.1016/j.softx.2025.102108","url":null,"abstract":"<div><div>The ability to simulate a neocortical neural network activity at the cellular level is of great interest in many studies. It allows for the investigation of microscopic mechanisms in both healthy and pathological brains. Microscale models of cortical volumes already exist however they are either too complex to use or too phenomenological to portray accurate results. NeoCoMM(The Neocortical Computational Microscale model) is an innovative and realistic microscale software application. It offers a friendly graphical user interface that allows for the simulation of the intracellular (single cell) and extracellular (local field potential) neural activity of a cortical column. This software provides a realistic framework that can portray the neural activity and underlying cellular mechanisms related to different brain pathologies such as epilepsy. NeoCoMM is capable of (1) simulating the cortical tissue of three different species, (2) visualizing individual cell responses to external stimulation, (3) visualizing the corresponding local field potential, (4) studying the impact of the recording electrode features on simulated signals, and (5) testing various physiological and pathological hypotheses. While NeoCoMM was primarily developed for simulating epileptiform activity, it can also generate healthy brain rhythms or be adapted to other brain disorders.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102108"},"PeriodicalIF":2.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534986","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}

引用次数: 0

Version [1.1] - [MakeDecision: Online system for the graphical design of decision-making models in crisp and fuzzy environments]

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-03-03 DOI: 10.1016/j.softx.2025.102084

Jakub Więckowski , Wojciech Sałabun

{"title":"Version [1.1] - [MakeDecision: Online system for the graphical design of decision-making models in crisp and fuzzy environments]","authors":"Jakub Więckowski , Wojciech Sałabun","doi":"10.1016/j.softx.2025.102084","DOIUrl":"10.1016/j.softx.2025.102084","url":null,"abstract":"<div><div>This paper presents an extension of the MakeDecision application, significantly enhancing its capabilities for addressing multi-criteria decision problems and designing structural decision-making models. The updated tool includes a wide range of new techniques that broaden the comprehensiveness of calculations, enabling the development of more complex and robust decision models. These additions include new Multi-Criteria Decision Analysis (MCDA) methods for both crisp and fuzzy environments, as well as new distance metrics, normalization techniques, defuzzification methods, and correlation coefficients. A novel data visualization module has also been integrated to facilitate the interpretation of results. Furthermore, the application allows for the adjustment of decision models with specific measures characteristic of selected MCDA methods, such as the preference function in the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) method, the compromise solution in the Characteristic Objects Method (COMET) method, and the expected solution point in the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method. Enhanced validation procedures provide users with more detailed guidance, while the redesigned interface improves usability. This extension transforms MakeDecision from a simple decision model prototyping tool into a comprehensive workbench, enabling the creation and analysis of advanced structural decision-making models.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102084"},"PeriodicalIF":2.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534988","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}

引用次数: 0

LoDCalculator: A level of detail classification software for 3D models in the Blender environment

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-02-27 DOI: 10.1016/j.softx.2025.102107

Bruno Rodriguez-Garcia, Ines Miguel-Alonso, Henar Guillen-Sanz, Andres Bustillo

引用次数: 0