32 research outputs found
Modulation parameter estimation of LFM interference for direct sequence spread spectrum communication system in alpha-stable noise
The linear frequency modulation (LFM) interference is one of the typical broadband interferences in direct sequence spread spectrum (DSSS) communication system. In this article, a novel modulation parameter estimation method of LFM interference is proposed for the DSSS communication system in alpha-stable noise. To accurately estimate the modulation parameters, the alpha-stable noise should be eliminated first. Thus, we formulate a new generalized extended linear chirplet transform to suppress the alpha-stable noise, for a robust time-frequency, transformation of LFM interference is realized. Then, using the Radon transform, the maximum value after transformation and the chirp rate according to the angle related to the maximum value are estimated. In addition, a generalized Fourier transform is introduced to estimate the initial frequency of the LFM interference. For the performance analysis, the Cramér-Rao lower bounds of the estimated chirp rate and the initial frequency of the LFM interference in the presence of alpha-stable noise are derived. Moreover, the asymptotic properties of the modulation parameter estimator are analyzed. Simulation results demonstrate that the performance of the proposed parameter estimation method significantly outperforms existing methods, especially in a low SNR regime
Exploring the Design Space of Immersive Urban Analytics
Recent years have witnessed the rapid development and wide adoption of
immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft
HoloLens. These immersive devices have the potential to significantly extend
the methodology of urban visual analytics by providing critical 3D context
information and creating a sense of presence. In this paper, we propose an
theoretical model to characterize the visualizations in immersive urban
analytics. Further more, based on our comprehensive and concise model, we
contribute a typology of combination methods of 2D and 3D visualizations that
distinguish between linked views, embedded views, and mixed views. We also
propose a supporting guideline to assist users in selecting a proper view under
certain circumstances by considering visual geometry and spatial distribution
of the 2D and 3D visualizations. Finally, based on existing works, possible
future research opportunities are explored and discussed.Comment: 23 pages,11 figure
A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
Inspired by the great success of machine learning (ML), researchers have
applied ML techniques to visualizations to achieve a better design,
development, and evaluation of visualizations. This branch of studies, known as
ML4VIS, is gaining increasing research attention in recent years. To
successfully adapt ML techniques for visualizations, a structured understanding
of the integration of ML4VISis needed. In this paper, we systematically survey
88 ML4VIS studies, aiming to answer two motivating questions: "what
visualization processes can be assisted by ML?" and "how ML techniques can be
used to solve visualization problems?" This survey reveals seven main processes
where the employment of ML techniques can benefit visualizations:Data
Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS
Interaction, VIS Reading, and User Profiling. The seven processes are related
to existing visualization theoretical models in an ML4VIS pipeline, aiming to
illuminate the role of ML-assisted visualization in general
visualizations.Meanwhile, the seven processes are mapped into main learning
tasks in ML to align the capabilities of ML with the needs in visualization.
Current practices and future opportunities of ML4VIS are discussed in the
context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are
still needed in the area of ML4VIS, we hope this paper can provide a
stepping-stone for future exploration. A web-based interactive browser of this
survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table
A joint multi user detection scheme for UWB sensor networks using waveform division multiple access
A joint multiuser detection (MUD) scheme for wireless sensor networks (WSNs) is proposed to suppress multiple access interference (MAI) caused by a large number of sensor nodes. In WSNs, waveform division multiple access ultra-wideband (WDMA-UWB) technology is well-suited for robust communications. Multiple sensor nodes are allowed to transmit modulated signals by sharing the same time periods and frequency bands using orthogonal pulse waveforms. This paper employs a mapping function based on the optimal multiuser detection (OMD) to map the received bits into the mapping space where error bits can be distinguished. In order to revise error bits caused by MAI, the proposed joint MUD scheme combines the mapping function with suboptimal algorithms. Numerical results demonstrate that the proposed MUD scheme provides good performances in terms of suppressing MAI and resisting near-far effect with low computational complexity
Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline
Designers need to consider not only perceptual effectiveness but also visual
styles when creating an infographic. This process can be difficult and time
consuming for professional designers, not to mention non-expert users, leading
to the demand for automated infographics design. As a first step, we focus on
timeline infographics, which have been widely used for centuries. We contribute
an end-to-end approach that automatically extracts an extensible timeline
template from a bitmap image. Our approach adopts a deconstruction and
reconstruction paradigm. At the deconstruction stage, we propose a multi-task
deep neural network that simultaneously parses two kinds of information from a
bitmap timeline: 1) the global information, i.e., the representation, scale,
layout, and orientation of the timeline, and 2) the local information, i.e.,
the location, category, and pixels of each visual element on the timeline. At
the reconstruction stage, we propose a pipeline with three techniques, i.e.,
Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an
extensible template from the infographic, by utilizing the deconstruction
results. To evaluate the effectiveness of our approach, we synthesize a
timeline dataset (4296 images) and collect a real-world timeline dataset (393
images) from the Internet. We first report quantitative evaluation results of
our approach over the two datasets. Then, we present examples of automatically
extracted templates and timelines automatically generated based on these
templates to qualitatively demonstrate the performance. The results confirm
that our approach can effectively extract extensible templates from real-world
timeline infographics.Comment: 10 pages, Automated Infographic Design, Deep Learning-based Approach,
Timeline Infographics, Multi-task Mode
A robust modulation classification method using convolutional neural networks
Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized
Visual analysis of discrimination in machine learning
The growing use of automated decision-making in critical applications, such
as crime prediction and college admission, has raised questions about fairness
in machine learning. How can we decide whether different treatments are
reasonable or discriminatory? In this paper, we investigate discrimination in
machine learning from a visual analytics perspective and propose an interactive
visualization tool, DiscriLens, to support a more comprehensive analysis. To
reveal detailed information on algorithmic discrimination, DiscriLens
identifies a collection of potentially discriminatory itemsets based on causal
modeling and classification rules mining. By combining an extended Euler
diagram with a matrix-based visualization, we develop a novel set visualization
to facilitate the exploration and interpretation of discriminatory itemsets. A
user study shows that users can interpret the visually encoded information in
DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens
provides informative guidance in understanding and reducing algorithmic
discrimination
VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching
Badminton is a fast-paced sport that requires a strategic combination of
spatial, temporal, and technical tactics. To gain a competitive edge at
high-level competitions, badminton professionals frequently analyze match
videos to gain insights and develop game strategies. However, the current
process for analyzing matches is time-consuming and relies heavily on manual
note-taking, due to the lack of automatic data collection and appropriate
visualization tools. As a result, there is a gap in effectively analyzing
matches and communicating insights among badminton coaches and players. This
work proposes an end-to-end immersive match analysis pipeline designed in close
collaboration with badminton professionals, including Olympic and national
coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive
analysis tool, that supports interactive badminton game analysis in an
immersive environment based on 3D reconstructed game views of the match video.
We propose a top-down analytic workflow that allows users to seamlessly move
from a high-level match overview to a detailed game view of individual rallies
and shots, using situated 3D visualizations and video. We collect 3D spatial
and dynamic shot data and player poses with computer vision models and
visualize them in VR. Through immersive visualizations, coaches can
interactively analyze situated spatial data (player positions, poses, and shot
trajectories) with flexible viewpoints while navigating between shots and
rallies effectively with embodied interaction. We evaluated the usefulness of
VIRD with Olympic and national-level coaches and players in real matches.
Results show that immersive analytics supports effective badminton match
analysis with reduced context-switching costs and enhances spatial
understanding with a high sense of presence.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphics
(IEEE VIS), 202
LassoNet:Deep Lasso-Selection of 3D Point Clouds
Selection is a fundamental task in exploratory analysis and visualization of
3D point clouds. Prior researches on selection methods were developed mainly
based on heuristics such as local point density, thus limiting their
applicability in general data. Specific challenges root in the great
variabilities implied by point clouds (e.g., dense vs. sparse), viewpoint
(e.g., occluded vs. non-occluded), and lasso (e.g., small vs. large). In this
work, we introduce LassoNet, a new deep neural network for lasso selection of
3D point clouds, attempting to learn a latent mapping from viewpoint and lasso
to point cloud regions. To achieve this, we couple user-target points with
viewpoint and lasso information through 3D coordinate transform and naive
selection, and improve the method scalability via an intention filtering and
farthest point sampling. A hierarchical network is trained using a dataset with
over 30K lasso-selection records on two different point cloud data. We conduct
a formal user study to compare LassoNet with two state-of-the-art
lasso-selection methods. The evaluations confirm that our approach improves the
selection effectiveness and efficiency across different combinations of 3D
point clouds, viewpoints, and lasso selections. Project Website:
https://lassonet.github.ioComment: 10 page