288 research outputs found
SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network
The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known
as drones, has generated a demand for reliable detection systems. The
inappropriate use of drones presents potential security and privacy hazards,
particularly concerning sensitive facilities. To overcome those obstacles, we
proposed the concept of MultiFeatureNet (MFNet), a solution that enhances
feature representation by capturing the most concentrated feature maps.
Additionally, we present MultiFeatureNet-Feature Attention (MFNet-FA), a
technique that adaptively weights different channels of the input feature maps.
To meet the requirements of multi-scale detection, we presented the versions of
MFNet and MFNet-FA, namely the small (S), medium (M), and large (L). The
outcomes reveal notable performance enhancements. For optimal bird detection,
MFNet-M (Ablation study 2) achieves an impressive precision of 99.8\%, while
for UAV detection, MFNet-L (Ablation study 2) achieves a precision score of
97.2\%. Among the options, MFNet-FA-S (Ablation study 3) emerges as the most
resource-efficient alternative, considering its small feature map size,
computational demands (GFLOPs), and operational efficiency (in frame per
second). This makes it particularly suitable for deployment on hardware with
limited capabilities. Additionally, MFNet-FA-S (Ablation study 3) stands out
for its swift real-time inference and multiple-object detection due to the
incorporation of the FA module. The proposed MFNet-L with the focus module
(Ablation study 2) demonstrates the most remarkable classification outcomes,
boasting an average precision of 98.4\%, average recall of 96.6\%, average mean
average precision (mAP) of 98.3\%, and average intersection over union (IoU) of
72.8\%. To encourage reproducible research, the dataset, and code for MFNet are
freely available as an open-source project:
github.com/ZeeshanKaleem/MultiFeatureNet.Comment: Paper accepted in IEEE TV
Artificial Intelligence for Multimedia Signal Processing
Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining
Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders
Unsupervised source separation involves unraveling an unknown set of source
signals recorded through a mixing operator, with limited prior knowledge about
the sources, and only access to a dataset of signal mixtures. This problem is
inherently ill-posed and is further challenged by the variety of time-scales
exhibited by sources in time series data. Existing methods typically rely on a
preselected window size that limits their capacity to handle multi-scale
sources. To address this issue, instead of operating in the time domain, we
propose an unsupervised multi-scale clustering and source separation framework
by leveraging wavelet scattering covariances that provide a low-dimensional
representation of stochastic processes, capable of distinguishing between
different non-Gaussian stochastic processes. Nested within this representation
space, we develop a factorial Gaussian-mixture variational autoencoder that is
trained to (1) probabilistically cluster sources at different time-scales and
(2) independently sample scattering covariance representations associated with
each cluster. Using samples from each cluster as prior information, we
formulate source separation as an optimization problem in the wavelet
scattering covariance representation space, resulting in separated sources in
the time domain. When applied to seismic data recorded during the NASA InSight
mission on Mars, our multi-scale nested approach proves to be a powerful tool
for discriminating between sources varying greatly in time-scale, e.g.,
minute-long transient one-sided pulses (known as ``glitches'') and structured
ambient noises resulting from atmospheric activities that typically last for
tens of minutes. These results provide an opportunity to conduct further
investigations into the isolated sources related to atmospheric-surface
interactions, thermal relaxations, and other complex phenomena
SALAI-Net: species-agnostic local ancestry inference network
Availability and implementation: We provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).Local ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.
We present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models’ ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.This paper was published as part of a special issue financially supported by ECCB2022. Some of the computing for this project was performed on the Sherlock cluster at Stanford University. We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results. A.G.I. and D.M.M. received support from NIH under award R01HG010140.
Conflict of Interest: AGI is a co-founder of Galatea Bio Inc.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarObjectius de Desenvolupament Sostenible::3 - Salut i Benestar::3.4 - Per a 2030, reduir en un terç la mortalitat prematura per malalties no transmissibles, mitjançant la prevenció i el tractament, i promoure la salut mental i el benestarPostprint (published version
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