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