This work presents a compact and power-efficient kHz-range relaxation (RC) oscillator with robust performance against temperature and voltage variations. By deliberately introducing a negative-offset voltage into the comparator, an offset cancellation scheme leveraging chopping and piecewise charge-acceleration facilitates a low temperature coefficient. A low-power comparator with a tail resistor and a low oscillation amplitude improves the energy efficiency. The die area is compact by introducing leakage-based temperature compensation that eliminates bulky resistors and complex calibration. Prototyped in a 28-nm CMOS process and measured at 28.5 kHz, our oscillator occupies 0.0046 mm$^{2}$and dissipates 27.6 nW at a 0.8-V supply. The energy efficiency is 0.97 nW/kHz, and the temperature coefficient is 33.3 ppm/$^{\circ}$C over$-$40 to 85$^{\circ}$C with 1-point calibration. The corresponding FoM of 164.9 dB compares favorably with the recent arts. The start-up time is rapid, and the period settling time is within one cycle of$\sim$5.7$\mu$s. The Allan deviation is$\le $40 ppm for measurement intervals of$>$0.5 s.

In this brief, a charge-prestored 21.2-MHz relaxation oscillator is proposed for ultralow-power applications. It occupies only 0.0043 mm2in 0.18-$\mu \text{m}$CMOS by resistor reusing and is reference-free. The simulated temperature coefficient (TC) of the output frequency is 15.2 ppm/° from −30 °C to 125 °C. By generating an asymmetric capacitor charging swing, our charge-prestored technique reduces significantly the power consumed by the swing-boostingRCnetwork during the charging phase. Also, the R-RCstructure further improves the energy efficiency. The total power consumption of the oscillator core is$1.806 \mu \text{W}$at 0.8 V, corresponding to an energy efficiency of$0.085 \mu \text{W}$/MHz that compares favorably with the state of the art.

Online state-time trajectory planning in highly dynamic environments remains an unsolved problem due to the curse of dimensionality of the state-time space. Existing state-time planners are typically implemented based on randomized sampling approaches or path searching on discrete graphs. The smoothness, path clearance, or planning efficiency is sometimes not satisfying. In this work, we propose a gradient-based planner on the state-time space for online trajectory generation in highly dynamic environments. To enable the gradient-based optimization, we propose a Timed-ESDT that supports distance and gradient queries with state-time keys. Based on the Timed-ESDT, we also define a smooth prior and an obstacle likelihood function that are compatible with the state-time space. The trajectory planning is then formulated to a MAP problem and solved by an efficient numerical optimizer. Moreover, to improve the optimality of the planner, we also define a state-time graph and conduct path searching on it to find a better initialization for the optimizer. By integrating the graph searching, the planning quality is significantly improved. Experiments on simulated and benchmark datasets demonstrate the superior performance of our proposes method over conventional ones.

This paper presents a search-based partial motion planner for generating feasible trajectories of car-like robots in highly dynamic environments. The planner searches for smooth, safe, and near-time-optimal trajectories by exploring a state graph built on motion primitives. To enable fast online planning, we propose an efficient path searching algorithm based on the aggregation and pruning of motion primitives. We then propose a fast collision checking algorithm that takes into account the motions of moving obstacles. The algorithm linearizes relative motions between the robot and obstacles, and then checks collisions by calculating a point-line distance. Benefiting from the fast searching and collision checking algorithms, the planner can effectively explore the state-time space to generate near-time-optimal solutions. Experiments show that the proposed method can generate feasible trajectories within milliseconds while maintaining a higher success rate than up-to-date methods, which significantly demonstrates its advantages.

Accurate indoor positioning is urgent for critical location-based services. The approach based on visible light communication (VLC) is promising, as it can deliver high accuracy by sharing the LED lighting infrastructure. In this paper, we propose an EKF-based tightly-coupled visual-inertial fusion method for visible light positioning with an IMU and a rolling-shutter camera, aiming for improved positioning robustness under LED shortage. With the proposed method, we can relax the assumption on the minimum number of concurrently observable LEDs required for positioning from three to one. Meanwhile, we can accurately track the sensor pair’s global 3D pose in realtime. We evaluate our method by real-world experiments using a prototyping VLC network. The efficacy for VLC beaconing and 3D pose estimation, as well as the robustness under LED shortage, is verified by extensive experiments.

Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the "background" part with spatial-temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the "background" in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.