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This work presents ARD2, a framework that enables real-time through-wall surveillance using two aerial drones and an augmented reality (AR) device. ARD2 consists of two main steps: target direction estimation and contour reconstruction. In the first stage, ARD2 leverages geometric relationships between the drones, the user, and the target to project the target's direction onto the user's AR display. In the second stage, images from the drones are synthesized to reconstruct the target's contour, allowing the user to visualize the target behind walls. Experimental results demonstrate the system's accuracy in both direction estimation and contour reconstruction., Comment: 6 pages

The rapid decline in groundwater around the world poses a significant challenge to sustainable agriculture. To address this issue, agricultural managed aquifer recharge (Ag-MAR) is proposed to recharge the aquifer by artificially flooding agricultural lands using surface water. Ag-MAR requires a carefully selected flooding schedule to avoid affecting the oxygen absorption of crop roots. However, current Ag-MAR scheduling does not take into account complex environmental factors such as weather and soil oxygen, resulting in crop damage and insufficient recharging amounts. This paper proposes MARLP, the first end-to-end data-driven control system for Ag-MAR. We first formulate Ag-MAR as an optimization problem. To that end, we analyze four-year in-field datasets, which reveal the multi-periodicity feature of the soil oxygen level trends and the opportunity to use external weather forecasts and flooding proposals as exogenous clues for soil oxygen prediction. Then, we design a two-stage forecasting framework. In the first stage, it extracts both the cross-variate dependency and the periodic patterns from historical data to conduct preliminary forecasting. In the second stage, it uses weather-soil and flooding-soil causality to facilitate an accurate prediction of soil oxygen levels. Finally, we conduct model predictive control (MPC) for Ag-MAR flooding. To address the challenge of large action spaces, we devise a heuristic planning module to reduce the number of flooding proposals to enable the search for optimal solutions. Real-world experiments show that MARLP reduces the oxygen deficit ratio by 86.8% while improving the recharging amount in unit time by 35.8%, compared with the previous four years., Comment: Accepted by KDD 2024

A novel problem called satellite downlink scheduling problem (SDSP) under breakpoint resume mode (SDSP-BRM) is studied in our paper. Compared to the traditional SDSP where an imaging data has to be completely downloaded at one time, SDSP-BRM allows the data of an imaging data be broken into a number of pieces which can be downloaded in different playback windows. By analyzing the characteristics of SDSP-BRM, we first propose a mixed integer programming model for its formulation and then prove the NP-hardness of SDSP-BRM. To solve the problem, we design a simple and effective heuristic algorithm (SEHA) where a number of problem-tailored move operators are proposed for local searching. Numerical results on a set of well-designed scenarios demonstrate the efficiency of the proposed algorithm in comparison to the general purpose CPLEX solver. We conduct additional experiments to shed light on the impact of the segmental strategy on the overall performance of the proposed SEHA.

We describe the cavity-enhancement of frequency combs over a wide tuning range of 450-700 nm (> 7900 cm$^{-1}$), covering nearly the entire visible spectrum. Tunable visible frequency combs from a synchronously-pumped optical parametric oscillator are coupled into a 4-mirror, dispersion-managed cavity with a finesse of 600 to 1400. An intracavity absorption path length enhancement greater than 190 is obtained over the entire tuning range, while preserving intracavity spectral bandwidths capable of supporting sub-200 fs pulse durations. These tunable cavity-enhanced frequency combs can find many applications in nonlinear optics and spectroscopy.

We present a 100 MHz repetition rate Yb-fiber-laser-pumped synchronously pumped optical parametric oscillator (SPOPO) delivering tunable frequency combs covering almost the entire visible spectral range. By intracavity doubling both the signal and idler combs and using collinear residual pump light, nearly continuous tuning over the range of 420-700 nm is achieved with only small gaps near the OPO degeneracy condition. Output powers range from 10 mW to 200 mW, depending on wavelength, with pulse durations below 150 fs without external compression. Frequency locking of all three collinearly outcoupled combs (pump, doubled signal, and doubled idler) to a femtosecond enhancement cavity facilitates direct comparison of their optical phase noise and phase modulation transfer functions. In the singly-resonant OPO, optical phase modulation of the pump comb is transferred nearly completely to the non-resonant idler comb. This results in a resonant signal comb with reduced optical phase noise and also enables high-bandwidth modulation on the idler comb via phase modulation of the pump.

We present a new technique using a frequency comb laser and optical cavities for performing ultrafast transient absorption spectroscopy with improved sensitivity. Resonantly enhancing the probe pulses, we demonstrate a sensitivity of $\Delta$OD $ = 1 \times 10^{-9}/\sqrt{\mbox{Hz}}$ for averaging times as long as 30 s per delay point ($\Delta$OD$_{min} = 2 \times 10^{-10}$). Resonantly enhancing the pump pulses allows us to produce a high excitation fraction at high repetition-rate, so that signals can be recorded from samples with optical densities as low as OD $\approx 10^{-8}$, or column densities $< 10^{10}$ molecules/cm$^2$. This high sensitivity enables new directions for ultrafast spectroscopy.