Behavioral Data Analysis and Visualization System Base on UWB Interior Positioning Technology

To realize autonomous navigation of intelligent robots in a variety of settings, analysis and classification of places, and the ability to actively collect information are necessary. In this paper, visual data are organized into an orientation histogram to roughly express input images by extracting and cumulating straight lines according to direction angle. In addition, behavioral data are organized into a behavioral histogram by cumulating motions performed to avoid obstacles encountered while the robot is executing specified behavioral patterns. These visual and behavioral data are utilized as input data, and the probability that a place belongs to a specific class is calculated by designating the places already learnt by the robot as categories. The naïve Bayes classification method is employed, in which the probability is calculated that the input data belong to each specific category, and the category with the highest probability is then selected. The location of the robot is classified by merging the probabilities for visual and behavioral data. The experimental results are as follows. First, a comparison of behavioral patterns used by the robot to collect data about a place indicates that a rotational behavior pattern provides the best performance. Second, classification performance is more accurate with two types of input data than with a single type of data.

Design and development of data visualization systems in a workstation environment

In this study, we aimed to analyze the in-store behavior of customers at a home improvement retail company using data collected from Bluetooth Low Energy beacon devices installed on shelves and shopping carts within a selected store. The beacons were strategically placed on store shelves to ensure complete coverage, leaving no blind spots. To cover 18 departments spanning a total area of approximately 4,800 square meters, 99 beacons were deployed. The duration of stay in each department, the order of visits, and the absolute visit date and time were recorded in the database. To investigate the relationship between in-store behavior and purchase data, we combined customers' behavioral data with their purchase information. Correlation analysis revealed a positive relationship between visit duration and purchase amount, particularly in the Floor Deco, Paint, and Taps departments. Additionally, we visualized store-wide data using a network diagram, highlighting key shopping areas, customer flow patterns, and high-revenue departments. The problem was also formulated as a multi-class classification task, and LSTM and XGBoost algorithms were applied for comparative analysis. Experiments were conducted on both the original dataset and a cleaned version, utilizing two distinct data modeling approaches: one based solely on sequential department visits and another incorporating visit duration. The results showed that both models performed similarly on the noisy dataset, indicating that adding duration information did not improve learning. However, when trained on the cleaned dataset where short- duration visits were removed, LSTM models outperformed XGBoost, demonstrating a stronger ability to capture meaningful sequential patterns. These findings highlight the potential of BLE beacon technology in retail analytics, offering deeper insights into customer behavior and informing data-driven decision-making for store optimization and personalized marketing. Future work will focus on expanding the dataset and refining predictive models to further enhance the accuracy and applicability of in-store behavior analysis.

Under the background of big data era, ports have realized the informatization of transport data, and has accumulated a large amount of transport data. The potential value contained in the port transportation data needs to be excavated, and a large number of external data also needs to be obtained. However, the traditional statistical analysis system can no longer meet the demand. In order to fully tap the potential value of port data, this paper designs and implements a port data analysis and visualization system to help enterprises master the trend of material flow. The system development is based on Python language. Pandas is used for data analysis, Flask is used to implement the MVC framework, ECharts is used for chart display, and data is imported from the business database to the MySQL database.

In recent years, the combination of databases, data analysis, and Internet technologies has greatly enriched the functionalities of GIS and facilitated the dissemination of geospatial information, hi this paper, we propose a web-based data visualization and analysis system to manage the watershed of the Occoquan Basin in Northern Virginia. This system distinguishes itself from others in the following respects. First, it enables near real-time data collection and dissemination. Second, data-query tools are embedded in the system to support effective data analysis and visualization functionalities. In addition to regular queries, aggregation queries have been developed to provide summary views of historical data with various granularities. Third, the visualization and analysis tools can be accessed via the Internet. WWW technologies, such as active web pages and Java programming, provide user-friendly interfaces for browsing, plotting, comparing, and downloading information of interest, without the need of dedicated GIS software. This system can render information to support many other applications, including pollution control and biochemical detection.

In order to meet the needs of data analysis and visualization to assist mobile network operation decision-making, telecom operators have established several mobile network index analysis tools or platforms. However, the network data analysis efficiency of planning, construction, maintenance and optimization is still low, and demand-oriented visualization means are still insufficient. This paper designs a mobile network data analysis and visualization system. The designed system aims at addressing the problems that mobile network has various types of data. The designed system can make data easy to manage, improve the data analysis efficiency and the flexibility of data visualization for telecom operators.

A web based MDSplus data analysis and visualization system for EAST

This paper proposed a cost function of features based mode switching algorithm for indoor positioning to solve the problem of choosing the best positioning mode in the overlapping area. The experiments show that when the precisions of the alternative location modes are of no big difference, using this algorithm is much more reasonable. Along with the growth of urban scale, the probability of people's appearance in the commercial buildings, supermarkets, exhibition halls or gymnasiums and other large -scale buildings are higher. Whether doing business, buying food, visiting the exhibition or watching sports game, people need to know all kinds of location information. Location-based services (LBS) have been considered the second killer business, after the service of short messages. The main methods of current positioning in interior environment are WLAN, WSN, RFID, infrared, ultrasonic and UWB, etc.. Each interior location technology in positioning accuracy, cost, facilities and equipments are so varied, that is, each positioning technology has its own scope of application. Only in its scope of application, it is the ideal way. There is no location technology that is applicable for every situation. With more and more large-scale constructions been built, like the large theaters, stadiums, exhibition centers, a single positioning technology obviously can not satisfy the actual demands in such complex constructions. There is an inevitable need for the comprehensive utilization of a wide range of positioning technology. Thus, according to the actual positioning demands of large-scale buildings, we made a research on how to make effective use of a variety of positioning techniques and how to integrate these indoor location technologies. This article discussed positioning mode selection problem of hybrid positioning technology by WLAN and WSN in overlapping range in interior environment and proposed a cost function of features based mode switching algorithm for indoor positioning

Music streaming platforms dominate digital consumption but offer users limited and superficial insight into their own listening behaviors. This data opacity restricts user autonomy and reinforces a dependency on opaque algorithmic recommendations, creating a distinct need for transparent, user-centric analytical tools. This paper presents Listen, a web-based system developed through a Design Science Research methodology to address this gap. The system securely integrates with the Spotify API to retrieve granular, longitudinal user data and employs advanced data visualization techniques within a fully responsive, intuitive interface. The resulting artifact provides interactive dashboards that empower users to conduct in-depth analysis of their most-listened artists and tracks across customizable timeframes and to explore complex audio features through accessible graphical representations. Our findings demonstrate that by prioritizing usability and analytical depth, Listen successfully provides users with greater transparency and control over their personal data, serving as a robust model for bridging the critical gap between data availability and user insight in digital media platforms.

This paper presents the work of our group concerning cancer image analysis and modeling. The adopted strategy aims to build a complete system for analysis and visualization of DICOM tomographic data, offering a variety of annotation or automatic segmentation tools as well as tools for tumor growth simulation and visualization.

The combination of 3D acquisition (terrestrial and airborne LiDAR, structured light, structure-from-motion) and 2D imaging (photographic, multispectral, panoramic, orthorectified, reflectance transformation) techniques allows the geometry, appearance and other aspects of culturally significant sites to be objectively documented. Traditionally, these data are usually transformed into models such as 3D textured meshes before they are visualized or analyzed—an often time- and effort-intensive process. We propose a system for the direct visualization and analysis of such data, allowing the different aspects recorded to be layered together, and co-visualized with annotations and other relevant information. We describe the required technical foundations, including gigapoint and gigapixel visualization pipelines that enable the dynamic layering of high-resolution imagery over massive minimally-processed LiDAR point clouds that serve as the base spatial layer. In particular, we introduce the pointbuffer—a GPU-resident viewdependent point cache—as the foundation of our gigapoint pipeline, and outline the use of virtual texturing for draping of gigapixel imagery onto point clouds. Finally, we present case studies from sites in Jordan and Italy.

The authors present in this work a large-scale system for space-time visualization, monitoring, modeling and analysis of epidemic data using a Grid platform. This system, dubbed IntegraEPI, is capable to integrate data from heterogeneous databases related to epidemic analysis and to make available analytical and computational methods to increase the predicting capability of the public heath system, in order to optimize its activities and resources when dealing with epidemic outbreak and prevention. This system, differently of what has been proposed before, is integrated and consequently it enables the construction of detailed predictive models of the dynamics of disease spreading. With the help of IntegraEPI, Health authorities will be able to decide about a set of possible actions that will be previously tested in a virtual population interacting in an urban infrastructure, considering its environmental factors, and finally compare the simulated data to consolidated data of real epidemic dynamics.

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

Human-computer interaction for virtual-real fusion

Although there are a number of visualization systems to choose from when analyzing data, only a few of these allow for the integration of other visualization and analysis techniques. There are even fewer visualization toolkits, frameworks and developmental systems from which one can develop custom visualization applications. Even within the research community, scientists either use what they can from the available tools or start from scratch to define a program in which they are able to develop new or modified visualization techniques. This research presents a new general-purpose platform in which the design and experimentation of new visualization and analysis techniques is the focus, and where the sharing of and integration with other techniques becomes second nature. Here we introduce the Universal Visualization Platform (UVP) as a data visualization and analysis system supporting future research and modern discovery environments. Furthermore, we present a novel perspective on the Model-View-Controller programming paradigm that defines visualizations as views of models. Finally, we present a new layer model specifying the construction and manipulation of visualizations. Here, layers containing graphic elements that encode information are rendered together using composition operators to generate visual displays.