15,651 research outputs found
Wireless Interference Identification with Convolutional Neural Networks
The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural networks (CNNs). The CNN naively learns its features through
self-optimization during an extensive data-driven GPU-based training process.
We propose a CNN example which is based upon sensing snapshots with a limited
duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
approaches and has a classification accuracy greater than 95% for
signal-to-noise ratio of at least -5 dB
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders
Supervised multi-channel audio source separation requires extracting useful
spectral, temporal, and spatial features from the mixed signals. The success of
many existing systems is therefore largely dependent on the choice of features
used for training. In this work, we introduce a novel multi-channel,
multi-resolution convolutional auto-encoder neural network that works on raw
time-domain signals to determine appropriate multi-resolution features for
separating the singing-voice from stereo music. Our experimental results show
that the proposed method can achieve multi-channel audio source separation
without the need for hand-crafted features or any pre- or post-processing
An Experimental Platform for Multi-spacecraft Phase-Array Communications
The emergence of small satellites and CubeSats for interplanetary exploration
will mean hundreds if not thousands of spacecraft exploring every corner of the
solar-system. Current methods for communication and tracking of deep space
probes use ground based systems such as the Deep Space Network (DSN). However,
the increased communication demand will require radically new methods to ease
communication congestion. Networks of communication relay satellites located at
strategic locations such as geostationary orbit and Lagrange points are
potential solutions. Instead of one large communication relay satellite, we
could have scores of small satellites that utilize phase arrays to effectively
operate as one large satellite. Excess payload capacity on rockets can be used
to warehouse more small satellites in the communication network. The advantage
of this network is that even if one or a few of the satellites are damaged or
destroyed, the network still operates but with degraded performance. The
satellite network would operate in a distributed architecture and some
satellites maybe dynamically repurposed to split and communicate with multiple
targets at once. The potential for this alternate communication architecture is
significant, but this requires development of satellite formation flying and
networking technologies. Our research has found neural-network control
approaches such as the Artificial Neural Tissue can be effectively used to
control multirobot/multi-spacecraft systems and can produce human competitive
controllers. We have been developing a laboratory experiment platform called
Athena to develop critical spacecraft control algorithms and cognitive
communication methods. We briefly report on the development of the platform and
our plans to gain insight into communication phase arrays for space.Comment: 4 pages, 10 figures, IEEE Cognitive Communications for Aerospace
Applications Worksho
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI). These dynamic images are constructed from a sequence of depth maps
using bidirectional rank pooling to effectively capture the spatial-temporal
information. Such image-based representations enable us to fine-tune the
existing ConvNets models trained on image data for classification of depth
sequences, without introducing large parameters to learn. Upon the proposed
representations, a convolutional Neural networks (ConvNets) based method is
developed for gesture recognition and evaluated on the Large-scale Isolated
Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The
method achieved 55.57\% classification accuracy and ranked place in
this challenge but was very close to the best performance even though we only
used depth data.Comment: arXiv admin note: text overlap with arXiv:1608.0633
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