192 research outputs found
Structuring music collections by exploiting peers' processing
Music collections are structured in very different ways by different useres. There is not one general taxonomy, but individual, user-specific structures exist. Most users appreciate some support in structering their collection. A large variety of methods has been developed for textual collections. However, audio data are completely different. In this paper, we present a peer to peer scenario where a music collection is enhanced a set of audio data in a node of the user's taxonomy by retrieving (partial) taxonomies of peers. In order to classify audio data into a taxonomy features need to be extracted. Adopting feature extraction to a particular set of classes is effective but not efficient. Hence, we propose again to exploit what has allready been done. Wellsuited feature extraction for one classification task is transferred to similar tasks using a new distance measures. --
The development of an autonomous rendezvous and docking simulation using rapid integration and prototyping technology
A generic planar 3 degree of freedom simulation was developed that supports hardware in the loop simulations, guidance and control analysis, and can directly generate flight software. This simulation was developed in a small amount of time utilizing rapid prototyping techniques. The approach taken to develop this simulation tool, the benefits seen using this approach to development, and on-going efforts to improve and extend this capability are described. The simulation is composed of 3 major elements: (1) Docker dynamics model, (2) Dockee dynamics model, and (3) Docker Control System. The docker and dockee models are based on simple planar orbital dynamics equations using a spherical earth gravity model. The docker control system is based on a phase plane approach to error correction
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
Clustering traffic scenarios and detecting novel scenario types are required
for scenario-based testing of autonomous vehicles. These tasks benefit from
either good similarity measures or good representations for the traffic
scenarios. In this work, an expert-knowledge aided representation learning for
traffic scenarios is presented. The latent space so formed is used for
successful clustering and novel scenario type detection. Expert-knowledge is
used to define objectives that the latent representations of traffic scenarios
shall fulfill. It is presented, how the network architecture and loss is
designed from these objectives, thereby incorporating expert-knowledge. An
automatic mining strategy for traffic scenarios is presented, such that no
manual labeling is required. Results show the performance advantage compared to
baseline methods. Additionally, extensive analysis of the latent space is
performed.Comment: Copyright 2022 IEEE. Personal use of this material is permitted.
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Communication Centric Design in Complex Automotive Embedded Systems
Automotive embedded applications like the engine management system are composed of multiple functional components that are tightly coupled via numerous communication dependencies and intensive data sharing, while also having real-time requirements. In order to cope with complexity, especially in multi-core settings, various communication mechanisms are used to ensure data consistency and temporal determinism along functional cause-effect chains. However, existing timing analysis methods generally only support very basic communication models that need to be extended to handle the analysis of industry grade problems which involve more complex communication semantics. In this work, we give an overview of communication semantics used in the automotive industry and the different constraints to be considered in the design process. We also propose a method for model transformation to increase the expressiveness of current timing analysis methods enabling them to work with more complex communication semantics. We demonstrate this transformation approach for concrete implementations of two communication semantics, namely, implicit and LET communication. We discuss the impact on end-to-end latencies and communication overheads based on a full blown engine management system
Quantum Fluctuations Approach to the Nonequilibrium -Approximation II: Density Correlations and Dynamic Structure Factor
The quantum dynamics of correlated fermionic or bosonic many-body systems
following external excitation can be successfully studied using nonequilibrium
Green functions (NEGF) or reduced density matrix methods. Approximations are
introduced via a proper choice of the many-particle selfenergy or decoupling of
the BBGKY-hierarchy, respectively. These approximations are based on Feynman's
diagram approaches or on cluster expansions into single-particle and
correlation operators. In a recent paper [E. Schroedter, J.-P. Joost, and M.
Bonitz, Cond. Matt. Phys. \textbf{25}, 23401 (2022)] we have presented a
different approach where, instead of equations of motion for the many-particle
NEGF (or density operators), equations for the correlation functions of
fluctuations are analyzed. In particular, we derived the stochastic GW and
polarization approximations that are closely related to the nonequilibrium GW
approximation. Here, we extend this approach to the computation of two-time
observables depending on the specific ordering of the underlying operators. In
particular, we apply this extension to the calculation of the density
correlation function and dynamic structure factor of correlated Hubbard
clusters in and out of equilbrium
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