18 research outputs found
Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation
Event-based cameras record an asynchronous stream of per-pixel brightness
changes. As such, they have numerous advantages over the standard frame-based
cameras, including high temporal resolution, high dynamic range, and no motion
blur. Due to the asynchronous nature, efficient learning of compact
representation for event data is challenging. While it remains not explored the
extent to which the spatial and temporal event "information" is useful for
pattern recognition tasks. In this paper, we focus on single-layer
architectures. We analyze the performance of two general problem formulations:
the direct and the inverse, for unsupervised feature learning from local event
data (local volumes of events described in space-time). We identify and show
the main advantages of each approach. Theoretically, we analyze guarantees for
an optimal solution, possibility for asynchronous, parallel parameter update,
and the computational complexity. We present numerical experiments for object
recognition. We evaluate the solution under the direct and the inverse problem
and give a comparison with the state-of-the-art methods. Our empirical results
highlight the advantages of both approaches for representation learning from
event data. We show improvements of up to 9 % in the recognition accuracy
compared to the state-of-the-art methods from the same class of methods
Online Weight-adaptive Nonlinear Model Predictive Control
Nonlinear Model Predictive Control (NMPC) is a powerful and widely used
technique for nonlinear dynamic process control under constraints. In NMPC, the
state and control weights of the corresponding state and control costs are
commonly selected based on human-expert knowledge, which usually reflects the
acceptable stability in practice. Although broadly used, this approach might
not be optimal for the execution of a trajectory with the lowest positional
error and sufficiently "smooth" changes in the predicted controls. Furthermore,
NMPC with an online weight update strategy for fast, agile, and precise
unmanned aerial vehicle navigation, has not been studied extensively. To this
end, we propose a novel control problem formulation that allows online updates
of the state and control weights. As a solution, we present an algorithm that
consists of two alternating stages: (i) state and command variable prediction
and (ii) weights update. We present a numerical evaluation with a comparison
and analysis of different trade-offs for the problem of quadrotor navigation.
Our computer simulation results show improvements of up to 70% in the accuracy
of the executed trajectory compared to the standard solution of NMPC with fixed
weights
Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
We propose a new computationally efficient privacy-preserving identification
framework based on layered sparse coding. The key idea of the proposed
framework is a sparsifying transform learning with ambiguization, which
consists of a trained linear map, a component-wise nonlinearity and a privacy
amplification. We introduce a practical identification framework, which
consists of two phases: public and private identification. The public untrusted
server provides the fast search service based on the sparse privacy protected
codebook stored at its side. The private trusted server or the local client
application performs the refined accurate similarity search using the results
of the public search and the layered sparse codebooks stored at its side. The
private search is performed in the decoded domain and also the accuracy of
private search is chosen based on the authorization level of the client. The
efficiency of the proposed method is in computational complexity of encoding,
decoding, "encryption" (ambiguization) and "decryption" (purification) as well
as storage complexity of the codebooks.Comment: EUSIPCO 201