61 research outputs found
Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing
We address the challenge of tracking an unknown number of targets in strong
clutter environments using measurements from a radar sensor. Leveraging the
range-Doppler spectra information, we identify the measurement classes, which
serve as additional information to enhance clutter rejection and data
association, thus bolstering the robustness of target tracking. We first
introduce a novel neural enhanced message passing approach, where the beliefs
obtained by the unified message passing are fed into the neural network as
additional information. The output beliefs are then utilized to refine the
original beliefs. Then, we propose a classification-aided robust multiple
target tracking algorithm, employing the neural enhanced message passing
technique. This algorithm is comprised of three modules: a message-passing
module, a neural network module, and a Dempster-Shafer module. The
message-passing module is used to represent the statistical model by the factor
graph and infers target kinematic states, visibility states, and data
associations based on the spatial measurement information. The neural network
module is employed to extract features from range-Doppler spectra and derive
beliefs on whether a measurement is target-generated or clutter-generated. The
Dempster-Shafer module is used to fuse the beliefs obtained from both the
factor graph and the neural network. As a result, our proposed algorithm adopts
a model-and-data-driven framework, effectively enhancing clutter suppression
and data association, leading to significant improvements in multiple target
tracking performance. We validate the effectiveness of our approach using both
simulated and real data scenarios, demonstrating its capability to handle
challenging tracking scenarios in practical radar applications.Comment: 15 page
Stratified Rule-Aware Network for Abstract Visual Reasoning
Abstract reasoning refers to the ability to analyze information, discover
rules at an intangible level, and solve problems in innovative ways. Raven's
Progressive Matrices (RPM) test is typically used to examine the capability of
abstract reasoning. The subject is asked to identify the correct choice from
the answer set to fill the missing panel at the bottom right of RPM (e.g., a
33 matrix), following the underlying rules inside the matrix. Recent
studies, taking advantage of Convolutional Neural Networks (CNNs), have
achieved encouraging progress to accomplish the RPM test. However, they partly
ignore necessary inductive biases of RPM solver, such as order sensitivity
within each row/column and incremental rule induction. To address this problem,
in this paper we propose a Stratified Rule-Aware Network (SRAN) to generate the
rule embeddings for two input sequences. Our SRAN learns multiple granularity
rule embeddings at different levels, and incrementally integrates the
stratified embedding flows through a gated fusion module. With the help of
embeddings, a rule similarity metric is applied to guarantee that SRAN can not
only be trained using a tuplet loss but also infer the best answer efficiently.
We further point out the severe defects existing in the popular RAVEN dataset
for RPM test, which prevent from the fair evaluation of the abstract reasoning
ability. To fix the defects, we propose an answer set generation algorithm
called Attribute Bisection Tree (ABT), forming an improved dataset named
Impartial-RAVEN (I-RAVEN for short). Extensive experiments are conducted on
both PGM and I-RAVEN datasets, showing that our SRAN outperforms the
state-of-the-art models by a considerable margin.Comment: AAAI 2021 paper. Code: https://github.com/husheng12345/SRA
Passively Q-switched erbium-doped fiber laser using evanescent field interaction with gold-nanosphere based saturable absorber
We demonstrate an all-fiber passively Q-switched erbiumdoped fiber laser (EDFL) using a gold-nanosphere (GNS) based saturable absorber (SA) with evanescent field interaction. Using the interaction of evanescent field for fabricating SAs, long nonlinear interaction length of evanescent wave and GNSs can be achieved. The GNSs are synthesized from mixing solution of chloroauricacid (HAuCl4) and sodium citrate by the heating effects of the microfiber's evanescent field radiation. The proposed passively Q-switched EDFL could give output pulses at 1562 nm with pulse width of 1.78 μs, a repetition rate of 58.1 kHz, a pulse energy of 133 nJ and a output power of 7.7 mWwhen pumped by a 980 nm laser diode of 237 mW
AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
Metaheuristics are prominent gradient-free optimizers for solving hard
problems that do not meet the rigorous mathematical assumptions of analytical
solvers. The canonical manual optimizer design could be laborious, untraceable
and error-prone, let alone human experts are not always available. This arises
increasing interest and demand in automating the optimizer design process. In
response, this paper proposes AutoOptLib, the first platform for accessible
automated design of metaheuristic optimizers. AutoOptLib leverages computing
resources to conceive, build up, and verify the design choices of the
optimizers. It requires much less labor resources and expertise than manual
design, democratizing satisfactory metaheuristic optimizers to a much broader
range of researchers and practitioners. Furthermore, by fully exploring the
design choices with computing resources, AutoOptLib has the potential to
surpass human experience, subsequently gaining enhanced performance compared
with human problem-solving. To realize the automated design, AutoOptLib
provides 1) a rich library of metaheuristic components for continuous,
discrete, and permutation problems; 2) a flexible algorithm representation for
evolving diverse algorithm structures; 3) different design objectives and
techniques for different optimization scenarios; and 4) a graphic user
interface for accessibility and practicability. AutoOptLib is fully written in
Matlab/Octave; its source code and documentation are available at
https://github.com/qz89/AutoOpt and https://AutoOpt.readthedocs.io/,
respectively
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