有没有相关的论文或文献参考？
这个数据集是基于什么背景创建的？
数据集的作者是谁？
能帮我联系到这个数据集的作者吗？
这个数据集如何下载？
DeepCIR|UWB技术数据集|室内导航数据集
收藏github2024-05-10 更新2024-05-31 收录
下载链接：
https://github.com/vutran86/DeepCIR
下载链接
链接失效反馈资源简介：
DeepCIR数据集专注于基于CIR的数据驱动UWB错误缓解，用于室内导航。数据集包含CIR缓冲区（120样本）和错误标签（以米为单位），用于训练和测试。数据集文件包括CIR数据和标签文件，详细记录了错误距离、估计距离和真实距离等信息。
创建时间：
2022-07-22
原始信息汇总
数据集概述
数据集名称
DeepCIR: Insights into CIR-based Data-driven UWB Error Mitigation for Indoor Navigation
数据集内容
- 训练数据：位于
dataset/train目录下，每个数据文件包含120样本的CIR缓冲区，每个标签文件包含以米为单位的误差。 - 标签文件格式：
- 双标签文件：Error(米), Estimated distance(米 -- 传感器估计的原始值), Groundtruth distance(米), Poll FP index, Resp FP index, Final FP index
- 单标签文件：Error(米), Estimated distance(米 -- 传感器估计的原始值), Groundtruth distance(米), FP index
数据集结构
- 原始数据：需从Google Drive下载并解压至
dataset/raw目录。 - 处理脚本：包括
combine.py,syncSessions.py,syncTrjectory.py等，用于数据提取和同步。 - 数据同步：
train_cir_poll.npy,train_cir_resp.npy,train_cir_final.npy同步，确保同一索引的数据属于同一事务。
数据文件格式
- 元数据：前48字节，包括记录时间戳、节点地址、测量有效性及距离测量。
- 时间戳：第48至78字节，记录6个时间戳。
- 诊断值：第78至95字节，包括First Path Index等。
- CIR数据：第98字节开始，包含120个样本的实部和虚部。
联系方式
- 联系人：Vu Tran
- 邮箱：vu.tran.apollo@gmail.com
AI搜集汇总
数据集介绍
构建方式
DeepCIR数据集的构建基于超宽带（UWB）技术，旨在通过采集室内导航中的信道冲激响应（CIR）数据来实现误差校正。数据集的构建过程包括从多个UWB传感器中提取原始数据，并通过同步脚本对这些数据进行精细的时间戳同步。每个数据文件包含120个样本的CIR缓冲区，而标签文件则包含以米为单位的误差信息。此外，数据集还提供了估计距离和真实距离的对比，以及用于进一步分析的FP索引信息。
特点
DeepCIR数据集的显著特点在于其精细的时间同步机制和多维度的数据结构。数据集不仅包含了CIR缓冲区的原始数据，还提供了详细的误差信息、估计距离与真实距离的对比，以及用于模型训练的FP索引。此外，数据集支持双边CIR（double-sided CIR）的分析，这对于复杂室内环境的误差校正具有重要意义。
使用方法
使用DeepCIR数据集时，用户需先从Google Drive下载原始数据并解压至指定目录。随后，通过运行提供的同步脚本，用户可以提取特定会话的数据并进行同步处理。数据集中的每个文件都包含了详细的元数据和CIR数据，用户可以根据需要提取这些信息进行模型训练或误差分析。数据集还提供了多种基线模型和FMCIR、WMCIR模型，用户可以直接使用或进行进一步的模型优化。
背景与挑战
背景概述
DeepCIR数据集由Vu Tran主导开发，专注于基于信道冲激响应（CIR）的数据驱动超宽带（UWB）误差缓解技术，特别适用于室内导航领域。该数据集的核心研究问题是如何通过分析CIR数据来有效减少UWB在室内环境中的定位误差。数据集的构建旨在为研究人员提供一个标准化的平台，以测试和验证基于CIR的误差缓解算法。通过提供详细的CIR缓冲区和误差标签，DeepCIR为室内导航中的UWB技术提供了新的研究视角，推动了该领域的技术进步。
当前挑战
DeepCIR数据集在构建过程中面临多项挑战。首先，数据集的采集涉及复杂的UWB信号处理和同步技术，确保每个CIR缓冲区与相应的误差标签精确匹配。其次，数据集的规模庞大，处理和存储这些数据需要高效的算法和计算资源。此外，由于UWB信号在室内环境中的多路径效应和噪声干扰，提取有效的CIR特征并准确估计误差是一个技术难题。最后，数据集的公开和共享也面临隐私和安全方面的考量，确保数据使用的合规性。
常用场景
经典使用场景
DeepCIR数据集在室内导航领域中被广泛应用于基于CIR（Channel Impulse Response）的超宽带（UWB）误差校正。通过分析CIR缓冲区数据，研究人员能够训练模型以预测和校正由UWB传感器测量的距离误差。这种数据驱动的误差校正方法显著提升了室内定位的精度和可靠性，尤其是在复杂多变的室内环境中。
实际应用
在实际应用中，DeepCIR数据集的误差校正模型被广泛应用于智能家居、工业自动化和无人驾驶等领域。例如，在智能家居中，通过提高UWB定位精度，可以实现更精准的设备控制和用户追踪；在工业自动化中，高精度的室内定位技术有助于提升生产效率和安全性；在无人驾驶领域，室内定位精度的提升为无人车辆的自主导航提供了重要支持。
衍生相关工作
基于DeepCIR数据集，研究人员开发了多种先进的误差校正模型，如FMCIR和WMCIR，这些模型在UWB测距误差校正中表现出色。此外，该数据集还激发了大量关于室内定位和UWB技术的研究，推动了相关领域的技术进步。例如，基于DeepCIR的研究成果已被应用于多篇学术论文中，进一步扩展了其在室内导航和无线通信领域的应用范围。
以上内容由AI搜集并总结生成
数据主题
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集
LFW
人脸数据集;LFW数据集共有13233张人脸图像，每张图像均给出对应的人名，共有5749人，且绝大部分人仅有一张图片。每张图片的尺寸为250X250，绝大部分为彩色图像，但也存在少许黑白人脸图片。 URL: http://vis-www.cs.umass.edu/lfw/index.html#download
AI_Studio 收录
VQA
我们提出了自由形式和开放式视觉问答 (VQA) 的任务。给定图像和关于图像的自然语言问题，任务是提供准确的自然语言答案。反映许多现实世界的场景，例如帮助视障人士，问题和答案都是开放式的。视觉问题有选择地针对图像的不同区域，包括背景细节和底层上下文。因此，与生成通用图像说明的系统相比，在 VQA 上取得成功的系统通常需要对图像和复杂推理有更详细的理解。此外，VQA 适合自动评估，因为许多开放式答案仅包含几个单词或一组封闭的答案，可以以多项选择的形式提供。我们提供了一个数据集包含 100，000 的图像和问题并讨论它提供的信息。提供了许多 VQA 基线，并与人类表现进行了比较。
OpenDataLab 收录
Alexa Domains
该数据集由前 100 万个网站的 URL 组成。 域名使用 Alexa 流量排名进行排名 是使用浏览行为的组合来确定的 网站上的用户数、唯一身份访问者的数量和网页浏览量。更详细地说，唯一身份访问者是 在给定日期访问网站的唯一用户数， 和 pageviews 是用户 URL 请求的总数 网站。但是，对同一网站的多个请求 在同一天被计为一次综合浏览量。网站 独立访问者和综合浏览量的最高组合 排名最高
OpenDataLab 收录
Canadian Census
**Overview** The data package provides demographics for Canadian population groups according to multiple location categories: Forward Sortation Areas (FSAs), Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs), Federal Electoral Districts (FEDs), Health Regions (HRs) and provinces. **Description** The data are available through the Canadian Census and the National Household Survey (NHS), separated or combined. The main demographic indicators provided for the population groups, stratified not only by location but also for the majority by demographical and socioeconomic characteristics, are population number, females and males, usual residents and private dwellings. The primary use of the data at the Health Region level is for health surveillance and population health research. Federal and provincial departments of health and human resources, social service agencies, and other types of government agencies use the information to monitor, plan, implement and evaluate programs to improve the health of Canadians and the efficiency of health services. Researchers from various fields use the information to conduct research to improve health. Non-profit health organizations and the media use the health region data to raise awareness about health, an issue of concern to all Canadians. The Census population counts for a particular geographic area representing the number of Canadians whose usual place of residence is in that area, regardless of where they happened to be on Census Day. Also included are any Canadians who were staying in that area on Census Day and who had no usual place of residence elsewhere in Canada, as well as those considered to be 'non-permanent residents'. National Household Survey (NHS) provides demographic data for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, census tracts, federal electoral districts and health regions. In order to provide a comprehensive overview of an area, this product presents data from both the NHS and the Census. NHS data topics include immigration and ethnocultural diversity; aboriginal peoples; education and labor; mobility and migration; language of work; income and housing. 2011 Census data topics include population and dwelling counts; age and sex; families, households and marital status; structural type of dwelling and collectives; and language. The data are collected for private dwellings occupied by usual residents. A private dwelling is a dwelling in which a person or a group of persons permanently reside. Information for the National Household Survey does not include information for collective dwellings. Collective dwellings are dwellings used for commercial, institutional or communal purposes, such as a hotel, a hospital or a work camp. **Benefits** - Useful for canada public health stakeholders, for public health specialist or specialized public and other interested parties. for health surveillance and population health research. for monitoring, planning, implementation and evaluation of health-related programs. media agencies may use the health regions data to raise awareness about health, an issue of concern to all canadians. giving the addition of longitude and latitude in some of the datasets the data can be useful to transpose the values into geographical representations. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset. **License Information** The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. **Included Datasets** - [Canadian Population and Dwelling by FSA 2011](https://www.johnsnowlabs.com/marketplace/canadian-population-and-dwelling-by-fsa-2011) - This Canadian Census dataset covers data on population, total private dwellings and private dwellings occupied by usual residents by forward sortation area (FSA). It is enriched with the percentage of the population or dwellings versus the total amount as well as the geographical area, province, and latitude and longitude. The whole Canada's population is marked as 100, referring to 100% for the percentages. - [Detailed Canadian Population Statistics by CMAs and CAs 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-cmas-and-cas-2011) - This dataset covers the population statistics of Canada by Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs). It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by FED 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-fed-2011) - This dataset covers the population statistics of Canada from 2011 by Federal Electoral District of 2013 Representation Order. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Health Region 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-health-region-2011) - This dataset covers the population statistics of Canada by health region. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Province 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-province-2011) - This dataset covers the population statistics of Canada by provinces and territories. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. **Data Engineering Overview** **We deliver high-quality data** - Each dataset goes through 3 levels of quality review - 2 Manual reviews are done by domain experts - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints - Data is normalized into one unified type system - All dates, unites, codes, currencies look the same - All null values are normalized to the same value - All dataset and field names are SQL and Hive compliant - Data and Metadata - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated - Data Updates - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted **Our data is curated and enriched by domain experts** Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts: - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations - The data is always kept up to date – even when the source requires manual effort to get updates - Support for data subscribers is provided directly by the domain experts who curated the data sets - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution. **Need Help?** If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com).
Databricks 收录
MultiTalk
MultiTalk数据集是由韩国科学技术院创建，包含超过420小时的2D视频，涵盖20种不同语言，旨在解决多语言环境下3D说话头生成的问题。该数据集通过自动化管道从YouTube收集，每段视频都配有语言标签和伪转录，部分视频还包含伪3D网格顶点。数据集的创建过程包括视频收集、主动说话者验证和正面人脸验证，确保数据质量。MultiTalk数据集的应用领域主要集中在提升多语言3D说话头生成的准确性和表现力，通过引入语言特定风格嵌入，使模型能够捕捉每种语言独特的嘴部运动。
arXiv 收录