248,280 research outputs found
Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures
Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification
Revealing static and dynamic modular architecture of the eukaryotic protein interaction network
In an effort to understand the dynamic organization of the protein interaction network and its role in the regulation of cell behavior, positioning of proteins into specific network localities was studied with respect to their expression dynamics. First, we find that constitutively expressed and dynamically co-regulated proteins cluster in distinct functionally specialized network neighborhoods to form static and dynamic functional modules, respectively. Then, we show that whereas dynamic modules are mainly responsible for condition-dependent regulation of cell behavior, static modules provide robustness to the cell against genetic perturbations or protein expression noise, and therefore may act as buffers of evolutionary as well as population variations in cell behavior. Observations in this study refine the previously proposed model of dynamic modularity in the protein interaction network, and propose a link between the evolution of gene expression regulation and biological robustness
Robustness of the Thirty Meter Telescope Primary Mirror Control System
The primary mirror control system for the Thirty Meter Telescope (TMT) maintains the alignment of the 492 segments in the presence of both quasi-static (gravity and thermal) and dynamic disturbances due to unsteady wind loads. The latter results in a desired control bandwidth of 1Hz at high spatial frequencies. The achievable bandwidth is limited by robustness to (i) uncertain telescope structural dynamics (control-structure interaction) and (ii) small perturbations in the ill-conditioned influence matrix that relates segment edge sensor response to actuator commands. Both of these effects are considered herein using models of TMT. The former is explored through multivariable sensitivity analysis on a reduced-order Zernike-basis representation of the structural dynamics. The interaction matrix ("A-matrix") uncertainty has been analyzed theoretically elsewhere, and is examined here for realistic amplitude perturbations due to segment and sensor installation errors, and gravity and thermal induced segment motion. The primary influence of A-matrix uncertainty is on the control of "focusmode"; this is the least observable mode, measurable only through the edge-sensor (gap-dependent) sensitivity to the dihedral angle between segments. Accurately estimating focus-mode will require updating the A-matrix as a function of the measured gap. A-matrix uncertainty also results in a higher gain-margin requirement for focus-mode, and hence the A-matrix and CSI robustness need to be understood simultaneously. Based on the robustness analysis, the desired 1 Hz bandwidth is achievable in the presence of uncertainty for all except the lowest spatial-frequency response patterns of the primary mirror
On Robustness Analysis of a Dynamic Average Consensus Algorithm to Communication Delay
This paper studies the robustness of a dynamic average consensus algorithm to
communication delay over strongly connected and weight-balanced (SCWB)
digraphs. Under delay-free communication, the algorithm of interest achieves a
practical asymptotic tracking of the dynamic average of the time-varying
agents' reference signals. For this algorithm, in both its continuous-time and
discrete-time implementations, we characterize the admissible communication
delay range and study the effect of the delay on the rate of convergence and
the tracking error bound. Our study also includes establishing a relationship
between the admissible delay bound and the maximum degree of the SCWB digraphs.
We also show that for delays in the admissible bound, for static signals the
algorithms achieve perfect tracking. Moreover, when the interaction topology is
a connected undirected graph, we show that the discrete-time implementation is
guaranteed to tolerate at least one step delay. Simulations demonstrate our
results
Multi-Object Tracking with Interacting Vehicles and Road Map Information
In many applications, tracking of multiple objects is crucial for a
perception of the current environment. Most of the present multi-object
tracking algorithms assume that objects move independently regarding other
dynamic objects as well as the static environment. Since in many traffic
situations objects interact with each other and in addition there are
restrictions due to drivable areas, the assumption of an independent object
motion is not fulfilled. This paper proposes an approach adapting a
multi-object tracking system to model interaction between vehicles, and the
current road geometry. Therefore, the prediction step of a Labeled
Multi-Bernoulli filter is extended to facilitate modeling interaction between
objects using the Intelligent Driver Model. Furthermore, to consider road map
information, an approximation of a highly precise road map is used. The results
show that in scenarios where the assumption of a standard motion model is
violated, the tracking system adapted with the proposed method achieves higher
accuracy and robustness in its track estimations
On dynamic network entropy in cancer
The cellular phenotype is described by a complex network of molecular
interactions. Elucidating network properties that distinguish disease from the
healthy cellular state is therefore of critical importance for gaining
systems-level insights into disease mechanisms and ultimately for developing
improved therapies. By integrating gene expression data with a protein
interaction network to induce a stochastic dynamics on the network, we here
demonstrate that cancer cells are characterised by an increase in the dynamic
network entropy, compared to cells of normal physiology. Using a fundamental
relation between the macroscopic resilience of a dynamical system and the
uncertainty (entropy) in the underlying microscopic processes, we argue that
cancer cells will be more robust to random gene perturbations. In addition, we
formally demonstrate that gene expression differences between normal and cancer
tissue are anticorrelated with local dynamic entropy changes, thus providing a
systemic link between gene expression changes at the nodes and their local
network dynamics. In particular, we also find that genes which drive
cell-proliferation in cancer cells and which often encode oncogenes are
associated with reductions in the dynamic network entropy. In summary, our
results support the view that the observed increased robustness of cancer cells
to perturbation and therapy may be due to an increase in the dynamic network
entropy that allows cells to adapt to the new cellular stresses. Conversely,
genes that exhibit local flux entropy decreases in cancer may render cancer
cells more susceptible to targeted intervention and may therefore represent
promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte
Genes Confer Similar Robustness to Environmental, Stochastic, and Genetic Perturbations in Yeast
Gene inactivation often has little or no apparent consequence for the phenotype of an organism. This property—enetic (or mutational) robustness—is pervasive, and has important implications for disease and evolution, but is not well understood. Dating back to at least Waddington, it has been suggested that mutational robustness may be related to the requirement to withstand environmental or stochastic perturbations. Here I show that global quantitative data from yeast are largely consistent with this idea. Considering the effects of mutations in all nonessential genes shows that genes that confer robustness to environmental or stochastic change also buffer the effects of genetic change, and with similar efficacy. This means that selection during evolution for environmental or stochastic robustness (also referred to as canalization) may frequently have the side effect of increasing genetic robustness. A dynamic environment may therefore promote the evolution of phenotypic complexity. It also means that “hub” genes in genetic interaction (synthetic lethal) networks are generally genes that confer environmental resilience and phenotypic stability
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