340,252 research outputs found
Hybrid 3D Localization for Visible Light Communication Systems
In this study, we investigate hybrid utilization of angle-of-arrival (AOA)
and received signal strength (RSS) information in visible light communication
(VLC) systems for 3D localization. We show that AOA-based localization method
allows the receiver to locate itself via a least squares estimator by
exploiting the directionality of light-emitting diodes (LEDs). We then prove
that when the RSS information is taken into account, the positioning accuracy
of AOA-based localization can be improved further using a weighted least
squares solution. On the other hand, when the radiation patterns of LEDs are
explicitly considered in the estimation, RSS-based localization yields highly
accurate results. In order to deal with the system of nonlinear equations for
RSS-based localization, we develop an analytical learning rule based on the
Newton-Raphson method. The non-convex structure is addressed by initializing
the learning rule based on 1) location estimates, and 2) a newly developed
method, which we refer as random report and cluster algorithm. As a benchmark,
we also derive analytical expression of the Cramer-Rao lower bound (CRLB) for
RSS-based localization, which captures any deployment scenario positioning in
3D geometry. Finally, we demonstrate the effectiveness of the proposed
solutions for a wide range of LED characteristics and orientations through
extensive computer simulations.Comment: Submitted to IEEE/OSA Journal of Lightwave Technology (10 pages, 14
figures
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
A Computational Study Of The Role Of Spatial Receptive Field Structure In Processing Natural And Non-Natural Scenes
The center-surround receptive field structure, ubiquitous in the visual system, is hypothesized to be evolutionarily advantageous in image processing tasks. We address the potential functional benefits and shortcomings of spatial localization and center-surround antagonism in the context of an integrate-and-fire neuronal network model with image-based forcing. Utilizing the sparsity of natural scenes, we derive a compressive-sensing framework for input image reconstruction utilizing evoked neuronal firing rates. We investigate how the accuracy of input encoding depends on the receptive field architecture, and demonstrate that spatial localization in visual stimulus sampling facilitates marked improvements in natural scene processing beyond uniformly-random excitatory connectivity. However, for specific classes of images, we show that spatial localization inherent in physiological receptive fields combined with information loss through nonlinear neuronal network dynamics may underlie common optical illusions, giving a novel explanation for their manifestation. In the context of signal processing, we expect this work may suggest new sampling protocols useful for extending conventional compressive sensing theory
SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments
Motivation: Chloroplasts are organelles found in plants and involved in several important cell processes. Similarly to other compartments in the cell, chloroplasts have an internal structure comprising several sub-compartments, where different proteins are targeted to perform their functions. Given the relation between protein function and localization, the availability of effective computational tools to predict protein sub-organelle localizations is crucial for large-scale functional studies.
Results: In this paper we present SChloro, a novel machine-learning approach to predict protein sub-chloroplastic localization, based on targeting signal detection and membrane protein information. The proposed approach performs multi-label predictions discriminating six chloroplastic sub-compartments that include inner membrane, outer membrane, stroma, thylakoid lumen, plastoglobule and thylakoid membrane. In comparative benchmarks, the proposed method outperforms current state-of-the-art methods in both single-and multi-compartment predictions, with an overall multi-label accuracy of 74%. The results demonstrate the relevance of the approach that is eligible as a good candidate for integration into more general large-scale annotation pipelines of protein subcellular localization
A new damage imaging method based on lamb wave wavenumber response and PZT 2D cross-shaped array
A new damage imaging method of composite structure based on Lamb wave wavenumber response and Piezoelectric Transducer (PZT) 2D cross-shaped array is proposed. The 2D cross-shaped array constructed by two linear PZT arrays is placed on composite structure to acquire Lamb wave damage scattering signal in spatial domain. For each linear PZT array, a wavenumber-time image of the damage scattering signal can be obtained by using spatial FFT and a time scanning process. Based on the two images, the wavenumbers of the damage scattering signal projecting at the two arrays can be obtained. By combining with the two projection wavenumbers, the damage can be localized without blind angle. The validation performed on a composite plate shows a good damage localization accuracy of this method
Structure and dynamics of the E. coli chemotaxis core signaling complex by cryo-electron tomography and molecular simulations
To enable the processing of chemical gradients, chemotactic bacteria possess large arrays of transmembrane chemoreceptors, the histidine kinase CheA, and the adaptor protein CheW, organized as coupled core-signaling units (CSU). Despite decades of study, important questions surrounding the molecular mechanisms of sensory signal transduction remain unresolved, owing especially to the lack of a high-resolution CSU structure. Here, we use cryo-electron tomography and sub-tomogram averaging to determine a structure of the Escherichia coli CSU at sub-nanometer resolution. Based on our experimental data, we use molecular simulations to construct an atomistic model of the CSU, enabling a detailed characterization of CheA conformational dynamics in its native structural context. We identify multiple, distinct conformations of the critical P4 domain as well as asymmetries in the localization of the P3 bundle, offering several novel insights into the CheA signaling mechanism
Discrimination of low-frequency tones employs temporal fine structure
An auditory neuron can preserve the temporal fine structure of a
low-frequency tone by phase-locking its response to the stimulus. Apart from
sound localization, however, little is known about the role of this temporal
information for signal processing in the brain. Through psychoacoustic studies
we provide direct evidence that humans employ temporal fine structure to
discriminate between frequencies. To this end we construct tones that are based
on a single frequency but in which, through the concatenation of wavelets, the
phase changes randomly every few cycles. We then test the frequency
discrimination of these phase-changing tones, of control tones without phase
changes, and of short tones that consist of a single wavelets. For carrier
frequencies below a few kilohertz we find that phase changes systematically
worsen frequency discrimination. No such effect appears for higher carrier
frequencies at which temporal information is not available in the central
auditory system.Comment: 12 pages, 3 figure
Measurement Matrix Design for Compressive Sensing Based MIMO Radar
In colocated multiple-input multiple-output (MIMO) radar using compressive
sensing (CS), a receive node compresses its received signal via a linear
transformation, referred to as measurement matrix. The samples are subsequently
forwarded to a fusion center, where an L1-optimization problem is formulated
and solved for target information. CS-based MIMO radar exploits the target
sparsity in the angle-Doppler-range space and thus achieves the high
localization performance of traditional MIMO radar but with many fewer
measurements. The measurement matrix is vital for CS recovery performance. This
paper considers the design of measurement matrices that achieve an optimality
criterion that depends on the coherence of the sensing matrix (CSM) and/or
signal-to-interference ratio (SIR). The first approach minimizes a performance
penalty that is a linear combination of CSM and the inverse SIR. The second one
imposes a structure on the measurement matrix and determines the parameters
involved so that the SIR is enhanced. Depending on the transmit waveforms, the
second approach can significantly improve SIR, while maintaining CSM comparable
to that of the Gaussian random measurement matrix (GRMM). Simulations indicate
that the proposed measurement matrices can improve detection accuracy as
compared to a GRMM
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