60 research outputs found
A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems
Integrated sensing and communications (ISAC) is emerging as a key technique
for next-generation wireless systems. In order to expedite the practical
implementation of ISAC within pervasive mobile networks, it is essential to
equip widely-deployed base stations with radar sensing capabilities. Thus, the
utilization of standardized multiple-input multiple-output (MIMO) orthogonal
frequency division multiplexing (OFDM) hardware architectures and waveforms
becomes pivotal for realizing seamless integration of effective communication
and sensing functionalities. In this paper, we introduce a novel joint
angle-range-velocity estimation algorithm for the MIMO-OFDM ISAC system. This
approach exclusively depends on conventional MIMO-OFDM communication waveforms,
which are widely adopted in wireless communications. Specifically, the
angle-range-velocity information of potential targets is jointly extracted by
utilizing all the received echo signals within a coherent processing interval
(CPI). Therefore, the proposed joint estimation algorithm can achieve larger
processing gains and higher resolution by fully exploiting echo signals and
jointly estimating the angle-range-velocity information. Theoretical analysis
for maximum unambiguous range, resolution, and processing gains are provided to
verify the advantages of the proposed joint estimation algorithm. Finally,
extensive numerical experiments are presented to demonstrate that the proposed
joint estimation approach can achieve significantly lower
root-mean-square-error (RMSE) of angle/range/velocity estimation for both
single-target and multi-target scenarios.Comment: 13 pages, 8 figures, submitted to IEEE Tran
Low-Range-Sidelobe Waveform Design for MIMO-OFDM ISAC Systems
Integrated sensing and communication (ISAC) is a promising technology in
future wireless systems owing to its efficient hardware and spectrum
utilization. In this paper, we consider a multi-input multi-output (MIMO)
orthogonal frequency division multiplexing (OFDM) ISAC system and propose a
novel waveform design to provide better radar ranging performance by taking
range sidelobe suppression into consideration. In specific, we aim to design
MIMO-OFDM dual-function waveform to minimize its integrated sidelobe level
(ISL) while satisfying the quality of service (QoS) requirements of multi-user
communications and the transmit power constraint. To achieve a lower ISL, the
symbol-level precoding (SLP) technique is employed to fully exploit the degrees
of freedom (DoFs) of the waveform design in both temporal and spatial domains.
An efficient algorithm utilizing majorization-minimization (MM) framework is
developed to solve the non-convex waveform design problem. Simulation results
reveal radar ranging performance improvement and demonstrate the benefits of
the proposed SLP-based low-range-sidelobe waveform design in ISAC systems
Retrieval-based Controllable Molecule Generation
Generating new molecules with specified chemical and biological properties
via generative models has emerged as a promising direction for drug discovery.
However, existing methods require extensive training/fine-tuning with a large
dataset, often unavailable in real-world generation tasks. In this work, we
propose a new retrieval-based framework for controllable molecule generation.
We use a small set of exemplar molecules, i.e., those that (partially) satisfy
the design criteria, to steer the pre-trained generative model towards
synthesizing molecules that satisfy the given design criteria. We design a
retrieval mechanism that retrieves and fuses the exemplar molecules with the
input molecule, which is trained by a new self-supervised objective that
predicts the nearest neighbor of the input molecule. We also propose an
iterative refinement process to dynamically update the generated molecules and
retrieval database for better generalization. Our approach is agnostic to the
choice of generative models and requires no task-specific fine-tuning. On
various tasks ranging from simple design criteria to a challenging real-world
scenario for designing lead compounds that bind to the SARS-CoV-2 main
protease, we demonstrate our approach extrapolates well beyond the retrieval
database, and achieves better performance and wider applicability than previous
methods.Comment: 29 page
Targeted Learning: A Hybrid Approach to Social Robot Navigation
Empowering robots to navigate in a socially compliant manner is essential for
the acceptance of robots moving in human-inhabited environments. Previously,
roboticists have developed classical navigation systems with decades of
empirical validation to achieve safety and efficiency. However, the many
complex factors of social compliance make classical navigation systems hard to
adapt to social situations, where no amount of tuning enables them to be both
safe (people are too unpredictable) and efficient (the frozen robot problem).
With recent advances in deep learning approaches, the common reaction has been
to entirely discard classical navigation systems and start from scratch,
building a completely new learning-based social navigation planner. In this
work, we find that this reaction is unnecessarily extreme: using a large-scale
real-world social navigation dataset, SCAND, we find that classical systems can
be used safely and efficiently in a large number of social situations (up to
80%). We therefore ask if we can rethink this problem by leveraging the
advantages of both classical and learning-based approaches. We propose a hybrid
strategy in which we learn to switch between a classical geometric planner and
a data-driven method. Our experiments on both SCAND and two physical robots
show that the hybrid planner can achieve better social compliance in terms of a
variety of metrics, compared to using either the classical or learning-based
approach alone
Nickel sulfide nanocrystals on nitrogen-doped porous carbon nanotubes with high-efficiency electrocatalysis for room-temperature sodium-sulfur batteries
Polysulfide dissolution and slow electrochemical kinetics of conversion reactions lead to low utilization of sulfur cathodes that inhibits further development of room-temperature sodium-sulfur batteries. Here we report a multifunctional sulfur host, NiS2 nanocrystals implanted in nitrogen-doped porous carbon nanotubes, which is rationally designed to achieve high polysulfide immobilization and conversion. Attributable to the synergetic effect of physical confinement and chemical bonding, the high electronic conductivity of the matrix, closed porous structure, and polarized additives of the multifunctional sulfur host effectively immobilize polysulfides. Significantly, the electrocatalytic behaviors of the Lewis base matrix and the NiS2 component are clearly evidenced by operando synchrotron X-ray diffraction and density functional theory with strong adsorption of polysulfides and high conversion of soluble polysulfides into insoluble Na2S2/Na2S. Thus, the as-obtained sulfur cathodes exhibit excellent performance in room-temperature Na/S batteries
Pre-matching study of the natural gas engine turbocharging system based on the coupling of experiments and numerical simulation
In this study, a pre-matching method was developed based on measured performance parameters and theoretical calculations of turbochargers. First, the turbocharger of a natural gas engine was subjected to a comprehensive performance experiment. According to the experimental results, the maximum efficiencies of the turbine and compressor are 70% and 75%, respectively, and the efficiency of the turbine drops sharply from 70% to 56.6% as the pressure ratio increases from 1.25 to 2.4. In this thesis, a specific turbocharger pre-matching software has been developed in conjunction with a database. Three turbines and three compressors were selected from the self-developed database for matching and comparative study using this method. The simulation results showed that the maximum efficiency of turbine #1, #2 and #3 is 71.3%, 72.2% and 72.7%, respectively, and the efficiency of these three turbines is concentrated between 65% and 72.5%. Obviously, the maximum efficiency of the turbine has increased by 1.3–2.7% and the overall efficiency has improved after the pre-matching. Therefore, this developed pre-matching method can reduce time cost, improve work efficiency and engine performance, and is important for the design and development of turbochargers
A High-Kinetics Sulfur Cathode with a Highly Efficient Mechanism for Superior Room-Temperature Na-S Batteries
2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Applications of room-temperature-sodium sulfur (RT-Na/S) batteries are currently impeded by the insulating nature of sulfur, the slow redox kinetics of sulfur with sodium, and the dissolution and migration of sodium polysulfides. Herein, a novel micrometer-sized hierarchical S cathode supported by FeS2 electrocatalyst, which is grown in situ in well-confined carbon nanocage assemblies, is presented. The hierarchical carbon matrix can provide multiple physical entrapment to polysulfides, and the FeS2 nanograins exhibit a low Na-ion diffusion barrier, strong binding energy, and high affinity for sodium polysulfides. Their combination makes it an ideal sulfur host to immobilize the polysulfides and achieve reversible conversion of polysulfides toward Na2S. Importantly, the hierarchical S cathode is suitable for large-scale production via the inexpensive and green spray-drying method. The porous hierarchical S cathode offers a high sulfur content of 65.5 wt%, and can deliver high reversible capacity (524 mAh g−1 over 300 cycles at 0.1 A g−1) and outstanding rate capability (395 mAh g−1 at 1 A g−1 for 850 cycles), holding great promise for both scientific research and real application
In vivo assessment of supra-cervical fetal membrane by MRI 3D CISS: A preliminary study
In approximately 8% of term births and 33% of pre-term births, the fetal membrane (FM) ruptures before delivery
Cryogenic in-memory computing using tunable chiral edge states
Energy-efficient hardware implementation of machine learning algorithms for
quantum computation requires nonvolatile and electrically-programmable devices,
memristors, working at cryogenic temperatures that enable in-memory computing.
Magnetic topological insulators are promising candidates due to their tunable
magnetic order by electrical currents with high energy efficiency. Here, we
utilize magnetic topological insulators as memristors (termed magnetic
topological memristors) and introduce a chiral edge state-based cryogenic
in-memory computing scheme. On the one hand, the chiral edge state can be tuned
from left-handed to right-handed chirality through spin-momentum locked
topological surface current injection. On the other hand, the chiral edge state
exhibits giant and bipolar anomalous Hall resistance, which facilitates the
electrical readout. The memristive switching and reading of the chiral edge
state exhibit high energy efficiency, high stability, and low stochasticity. We
achieve high accuracy in a proof-of-concept classification task using four
magnetic topological memristors. Furthermore, our algorithm-level and
circuit-level simulations of large-scale neural networks based on magnetic
topological memristors demonstrate a software-level accuracy and lower energy
consumption for image recognition and quantum state preparation compared with
existing memristor technologies. Our results may inspire further topological
quantum physics-based novel computing schemes.Comment: 33 pages, 12 figure
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