A Software-Defined-Radio Platform for Multiple-Input-Multiple-Output Over-The-Air Measurement

This paper presents a 2 × 2 multiple-inputmultiple-output over-the-air (MIMO OTA) measurement system with user-programmable, reconfigurable and real-time signal processing field-programmable gate arrays (FPGAs)-based software-defined radio (SDR) capability. Signal generation and analysis as well as channel emulation are all implemented using vector signal transceivers (VSTs). As a demonstration, we performed the Third Generation Partnership Project (3GPP) two-stage MIMO OTA conducted test using a downlink time division long-term evolution (TD-LTE) duplex scheme. The channel emulation was operated in a stochastic mode. Some preliminary results of the system verification are shown

InGVIO: A Consistent Invariant Filter for Fast and High-Accuracy GNSS-Visual-Inertial Odometry

Combining Global Navigation Satellite System (GNSS) with visual and inertial sensors can give smooth pose estimation without drifting in geographical coordinates. The fusion system gradually degrades to Visual-Inertial Odometry (VIO) with the number of satellites decreasing, which guarantees robust global navigation in GNSS unfriendly environments. In this letter, we propose an open-sourced invariant filter-based platform, InGVIO, to tightly fuse monocular/stereo visual-inertial measurements, along with raw data from GNSS, i.e. pseudo ranges and Doppler shifts. InGVIO gives highly competitive results in terms of accuracy and computational load compared to current graph-based and `naive' EKF-based algorithms. Thanks to our proposed key-frame marginalization strategies, the baseline for triangulation is large although only a few cloned poses are kept. Besides, landmarks are anchored to a single cloned pose to fit the nonlinear log-error form of the invariant filter while achieving decoupled propagation with IMU states. Moreover, we exploit the infinitesimal symmetries of the system, which gives equivalent results for the pattern of degenerate motions and the structure of unobservable subspaces compared to our previous work using observability analysis. We show that the properly-chosen invariant error captures such symmetries and has intrinsic consistency properties. InGVIO is tested on both open datasets and our proposed fixed-wing datasets with variable levels of difficulty. The latter, to the best of our knowledge, are the first datasets open-sourced to the community on a fixed-wing aircraft with raw GNSS.Comment: 8 pages, 8 figures; manuscript will be submitted to IEEE RA-L for possible publicatio

A LTE MIMO OTA Test System Using Vector Signal Transceivers

A 2 × 2 multiple-input-multiple-output over-the-air (MIMO OTA) test system based on four field-programmable Vector-Signal-Transceiver (VST) modules is presented. The system enables 2 x 2 MIMO OTA testing by assembling of a twochannel Evolved Node B (eNodeB) LTE base station emulator, a 2x2 channel emulator, and a two-channel user equipment (UE) simulator. A two-stage MIMO OTA test method has been demonstrated with downlink Long-Term Evolution Time-Division Duplex (LTE-TDD) mode using different modulation and coding schemes (MCSs). Test results and analysis are shown. This system will allow a systematic study of MIMO OTA metrology needs

Finding and Editing Multi-Modal Neurons in Pre-Trained Transformer

Multi-modal large language models (LLM) have achieved powerful capabilities for visual semantic understanding in recent years. However, little is known about how LLMs comprehend visual information and interpret different modalities of features. In this paper, we propose a new method for identifying multi-modal neurons in transformer-based multi-modal LLMs. Through a series of experiments, We highlight three critical properties of multi-modal neurons by four well-designed quantitative evaluation metrics. Furthermore, we introduce a knowledge editing method based on the identified multi-modal neurons, for modifying a specific token to another designative token. We hope our findings can inspire further explanatory researches on understanding mechanisms of multi-modal LLMs

Measurement-Based Characterization of 39 GHz Millimeter-Wave Dual-Polarized Channel Under Foliage Loss Impact

This paper presents a measurement-based analysis of wideband 39 GHz millimeter wave (mm-wave) dual-polarized propagation channel under the impact of foliage presence between a transmitter (Tx) and a receiver (Rx). The measurements were conducted in a rich-vegetation area, and the so-called direction-scan-sounding (DSS) method which rotates a horn antenna in angular domains was applied, aiming at investigating the direction-of-arrival (DoA)-dependent characteristics of polarimetric channels. Four Tx-to-Rx polarization configurations were considered, including co-polarization scenarios with vertical Tx-polarization to vertical Rx-polarization (VV) and horizontal to horizontal (HH), as well as cross-polarization with vertical to horizontal (VH) and horizontal to vertical (HV), which allow scrutinizing the differences in delay-direction dispersion for usually-encountered scenarios. A foliage loss model for various vegetation depths in VV polarization configuration, was also presented in this paper. The results show that the foliage-loss DoA spectra for VH and HV are similar, while the spectra exhibit less penetration loss in most directions for VV than for the HH. Furthermore, the presence of vegetation between the Tx and the Rx leads to larger dispersion in delay compared to the clear line-of-sight (LoS) scenario, particularly for vertical polarization in the Tx side, and additionally, the foliage presence also results in evident DoA dispersion, specially in the HV scenario. Selectivity in directions caused by foliage is more significant in vertically-polarized Tx scenarios than in the horizontally-polarized Tx scenarios. A statistical model is established summarizing these comparison details

Cooperative Spin Amplification

Quantum amplification is recognized as a key resource for precision measurements. However, most conventional paradigms employ an ensemble of independent particles that usually limit the performance of quantum amplification in gain, spectral linewidth, etc. Here we demonstrate a new signal amplification using cooperative 129Xe nuclear spins embedded within a feedback circuit, where the noble-gas spin coherence time is enhanced by at least one order of magnitude. Using such a technique, magnetic field can be substantially pre-enhanced by more than three orders and is in situ readout with an embedded 87Rb magnetometer. We realize an ultrahigh magnetic sensitivity of 4.0 fT/Hz$^{1/2}$ that surpasses the photon-shot noise and even below the spin-projection noise of the embedded atomic magnetometer, allowing for exciting applications including searches for dark matter with sensitivity well beyond supernova constraints. Our findings extend the physics of quantum amplification to cooperative spin systems and can be generalized to a wide variety of existing sensors, enabling a new class of cooperative quantum sensors.Comment: 7 pages, 4 figure

Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge

Uplift modeling is a fundamental component of marketing effect modeling, which is commonly employed to evaluate the effects of treatments on outcomes. Through uplift modeling, we can identify the treatment with the greatest benefit. On the other side, we can identify clients who are likely to make favorable decisions in response to a certain treatment. In the past, uplift modeling approaches relied heavily on the difference-in-difference (DID) architecture, paired with a machine learning model as the estimation learner, while neglecting the link and confidential information between features. We proposed a framework based on graph neural networks that combine causal knowledge with an estimate of uplift value. Firstly, we presented a causal representation technique based on CATE (conditional average treatment effect) estimation and adjacency matrix structure learning. Secondly, we suggested a more scalable uplift modeling framework based on graph convolution networks for combining causal knowledge. Our findings demonstrate that this method works effectively for predicting uplift values, with small errors in typical simulated data, and its effectiveness has been verified in actual industry marketing data.Comment: 6 pages, 6 figure

Focusing through dynamic tissue with millisecond digital optical phase conjugation

Digital optical phase conjugation (DOPC) is a new technique employed in wavefront shaping and phase conjugation for focusing light through or within scattering media such as biological tissues. DOPC is particularly attractive as it intrinsically achieves a high fluence reflectivity in comparison to nonlinear optical approaches. However, the slow refresh rate of liquid crystal spatial light modulators and limitations imposed by computer data transfer speeds have thus far made it difficult for DOPC to achieve a playback latency of shorter than ∼200  ms and, therefore, prevented DOPC from being practically applied to thick living samples. In this paper, we report a novel DOPC system that is capable of 5.3 ms playback latency. This speed improvement of almost 2 orders of magnitude is achieved by using a digital micromirror device, field programmable gate array (FPGA) processing, and a single-shot binary phase retrieval technique. With this system, we are able to focus through 2.3 mm living mouse skin with blood flowing through it (decorrelation time ∼30  ms) and demonstrate that the focus can be maintained indefinitely—an important technological milestone that has not been previously reported, to the best of our knowledge

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

The raw depth image captured by indoor depth sensors usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and the limited distance range. The incomplete depth map with missing values burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed to alleviate this issue. While most existing methods can generate accurate dense depth maps from sparse and uniformly sampled depth maps, they are not suitable for complementing large contiguous regions of missing depth values, which is common and critical in images captured in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. In the other branch, we propose an RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate the features across the two branches, and we append a confidence fusion head to fuse the two outputs of the branches for the final depth map. Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method clearly improves the depth completion performance, especially in a more realistic setting of indoor environments, with the help of our proposed pseudo depth maps in training.Comment: Haowen Wang and Zhengping Che are with equal contributions. Under review. An earlier version has been accepted by CVPR 2022 (arXiv:2203.10856