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openmit-licenseSep 2023View details →

Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset

In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>, but in a fast manner. <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#general-dataset-structure">General Dataset Structure</a> A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>. This implementation was carried out in the MATLAB software <a href="https://github.com/johanflorez98/WLANs-optimal-designs-methodology">WLANs-optimal-designs-methodology</a>. Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems. We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points. The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels. The pictures have a depth of 8 bits and size of 256pixels X 256pixels equivalents to indoor constructions of 20 X 20 m2. These ones make reference to offices's spaces at general or classroom. <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#obtained-models">Obtained Models</a> To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used. To manipulate the codes better, click here in the <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset">repository</a>.

opencc-by-4.0Dec 2023View details →

Рис. 4. Карта-схема мест встреч пятнистого оΛеня в Нижнем Приамурье в 1979–2021 гг. КваΑраты — места фоторегистрации: 1 — верховья рр. Обор и Àурмин; 2, 3 — Анюйский национаΛьный парк; круги — места встреч по Λитературным и опросным Αанным: 1 — окрестности с. Кутузовка (место первой регистрации в 1979 г.); 2 — верховья р. СиΑима; 3 — устье р. Нижняя Буге; 4 — бассейн р. Мухен; 5–8 — Анюйский национаΛьный парк (соответственно, р. Пихца, урочище Сира, окрестности с. Арсеньево, устье р. СоΛоми); 9 — среΑнее течение р. СоΛоми; 10 — 76 км трассы ΔиΑога — Ванино; 11 — бассейн р. Кия; 12 — бассейн р. ХойΑур; 13 — бассейн р. Нюра Fig. 4. A schematic map of sika deer sightings in the Lower Amur Region in 1979-2021. Squares designate sites of photo recording: 1 — upper reaches of the rivers Obor and Durmin; 2, 3 — Anyui National Park; circles designate sightings sites according to the literature and the survey data: 1 — vicinity of the village Kutuzovka (the place of the first registration in 1979); 2 — upper reaches of the river Sidima; 3 — the mouth of the river Lower Buge; 4 — the Mukhen River basin; 5-8 —Anyui National Park (respectively, the Pikhtsa River, the Sira tract, the vicinity of the village Arsenyevo, the mouth of the Solomi River); 9 — the middle course of the Solomi River; 10 — 76 km of the Lidoga-Vanino Highway; 11 — the Kiya River basin; 12 — the Khoydur River basin; 13 — the Nyura River basin in New data on the distribution of sika deer Cervus nippon Temminck, 1838 in the Lower Amur Region

Рис. 4. Карта-схема мест встреч пятнистого оΛеня в Нижнем Приамурье в 1979–2021 гг. КваΑраты — места фоторегистрации: 1 — верховья рр. Обор и Àурмин; 2, 3 — Анюйский национаΛьный парк; круги — места встреч по Λитературным и опросным Αанным: 1 — окрестности с. Кутузовка (место первой регистрации в 1979 г.); 2 — верховья р. СиΑима; 3 — устье р. Нижняя Буге; 4 — бассейн р. Мухен; 5–8 — Анюйский национаΛьный парк (соответственно, р. Пихца, урочище Сира, окрестности с. Арсеньево, устье р. СоΛоми); 9 — среΑнее течение р. СоΛоми; 10 — 76 км трассы ΔиΑога — Ванино; 11 — бассейн р. Кия; 12 — бассейн р. ХойΑур; 13 — бассейн р. Нюра Fig. 4. A schematic map of sika deer sightings in the Lower Amur Region in 1979-2021. Squares designate sites of photo recording: 1 — upper reaches of the rivers Obor and Durmin; 2, 3 — Anyui National Park; circles designate sightings sites according to the literature and the survey data: 1 — vicinity of the village Kutuzovka (the place of the first registration in 1979); 2 — upper reaches of the river Sidima; 3 — the mouth of the river Lower Buge; 4 — the Mukhen River basin; 5-8 —Anyui National Park (respectively, the Pikhtsa River, the Sira tract, the vicinity of the village Arsenyevo, the mouth of the Solomi River); 9 — the middle course of the Solomi River; 10 — 76 km of the Lidoga-Vanino Highway; 11 — the Kiya River basin; 12 — the Khoydur River basin; 13 — the Nyura River basin

Figure 5. A front view of the "Mary and John Gray Library"-Modeling, Designing, and Implementing an Avatar-based Interactive Map

Figure 5 represents the avatar standing outside and in front of the Mary and John Gray Library after selecting the option “Library”. The library’s main purpose is to facilitate students with a variety of scholarly information within the overall composition of the University’s stated mission. Figure 5 shows the path generated by A* algorithms with a red color.

Figure 3. The 'Welcome Screen' of our implementation-Modeling, Designing, and Implementing an Avatar-based Interactive Map

Figure 3 shows an avatar ready to start the game. The user has the option to click the button called ‘Go Cardinals! Start’ Button. Once that happens, the avatar gets to choose going to one of the buildings of interest mentioned above.

Figure 1. The Statechart for the Player movement and the Navigation System-Modeling, Designing, and Implementing an Avatar-based Interactive Map

The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.

Figure 2. The class diagram for the Player and Camera movements-Modeling, Designing, and Implementing an Avatar-based Interactive Map

Figure 4. An inside view of the "Maes building along with the Navigation Path" -Modeling, Designing, and Implementing an Avatar-based Interactive Map

Figure 4 represents the navigation path to the Department of Computer Science inside the Maes building after selecting the option “D.C.S.”, which stands for Department of Computer Science.

Figure 6. An inside view of Mary and John Gray Library-Modeling, Designing, and Implementing an Avatar-based Interactive Map

Figure 6 exhibits the ambience of the study environment that allows students to have group discussions, and when to access Internet, and more. The photographs have been digitized in a very realistic way.

opencc-by-4.0Nov 2019View details →

18X7 Human brain field maps for shim coil designs

128 B0 field maps of head brains of 18 different healthy adult volunteers in seven different head positions.

zenodo36/100

Replication package: 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study

Replication package | 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study

openother-openJun 2022View details →

zenodo36/100

Disaggregated soil map of the Designation of Origin Campo de Borja

Disaggregated soil map of the Designation of Origin Campo de Borja in GeoPackage format. It contains a single-polygon vector layer and a table of the map legend.

opencc-by-4.0Jul 2022View details →

zenodo36/100

Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints: Dataset and Code

This is the dataset and related code artifact for the article "Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints". Abstract of the article:    Microservice APIs are often identified and designed based on Domain-Driven Design (DDD). To help in the continuous analysis of mappings of domain model elements to APIs and API endpoints, we aim to automate the assessment of conformance to Architectural Design Decision (ADD) options. The ADDs, their decision options, and relevant decision drivers studied in this paper originate from an empirical study on the mapping of domain model elements to APIs and API endpoints in practice. We propose automated detectors to detect the decision options of the ADDs taken in a given microservice API model, and an assessment scoring scheme based on the empirical knowledge codified in the ADDs. We evaluate our work, by first manually creating a ground truth for 14 cases in a multi-case study, and then comparing the results of our automated detectors to the ground truth for each of the 14 cases. In the cases we were able to identify 86\% of the decision points correctly, and a statistical analysis of our data shows only a negligible effect size for differences to the ground truth.

opencc-by-4.0Aug 2021View details →

opencc-by-4.0Aug 2020View details →

Domain-specific Language for Data-driven Design Time Analyses and Result Mappings for Logic Programs - Data Set

This data set contains additional information to the master thesis of Sebastian Hahner. This includes the prototypical implementation of the domain-specific language (DSL) as well as sample scenarios and mapping results.

opencc-by-4.0Feb 2004View details →

Comprehensive Risk and Emergency Management Conceptual Design and Map (Response Process Big Picture)

Comprehensive Risk and Emergency Management Conceptual Design and Map helps to have an insight about Risk and Emergency Planning and Management Process to better act. Response Process Big Picture

opennotspecifiedDec 2012View details →

FIGURE 4. Distribution map for P in On the validity of Pelvicachromis sacrimontis Paulo, 1977 (Perciformes, Cichlidae), with designation of a neotype, and redescription of the species

FIGURE 4. Distribution map for P. sacrimontis, based on collection data of neotype and paraneotypes; unfilled dot = type locality for neotype.