MH-REACH-Mote: supporting multi-hop passive radio wake-up for wireless sensor network

A passive wake-up radio in a wireless sensor network (WSN) has the advantage of increasing network lifetime by using a wake-up radio receiver (WuRx) to eliminate unnecessary idle listening. A sensor node equipped with a WuRx can operate in an ultra-low-power sleep mode, waiting for a trigger signal sent by the wake-up radio transmitter (WuTx). The passive WuRx is entirely powered by the energy harvested from radio transmissions sent by the WuTx. Therefore, it has the advantage of not consuming any energy locally, which would drain the sensor node's battery. Even so, the high amount of energy required to wake up a passive WuRx by a WuTx makes it difficult to build a multi-hop passive wake-up sensor network. In this paper, we describe and discuss our implementation of a battery-powered sensor node with multi-hop wake-up capability using passive WuRxs, called MH-REACH-Mote (Multi-hop-Range EnhAnCing energy Harvester-Mote). The MH-REACH-Mote is kept in an ultra-low-power sleep mode until it receives a wake-up trigger signal. Upon receipt, it wakes up and transmits a new trigger signal to power other passive WuRxs. We evaluate the wake-up range and power consumption of an MH-REACH-Mote through a series of field tests. Results show that the MH-REACH-Mote enables multi-hop wake-up capabilities for passive WuRxs with a wake-up range of 9.4m while requiring a reasonable power consumption for WuTx functionality. We also simulate WSN data collection scenarios with MH-REACH-Motes and compare the results with those of active wake-up sensor nodes as well as a low power listening approach. The results show that the MH-REACH-Mote enables a longer overall lifetime than the other two approaches when data is collected infrequently.Peer ReviewedPostprint (author's final draft

Amortized Network Intervention to Steer the Excitatory Point Processes

We tackle the challenge of large-scale network intervention for guiding excitatory point processes, such as infectious disease spread or traffic congestion control. Our model-based reinforcement learning utilizes neural ODEs to capture how the networked excitatory point processes will evolve subject to the time-varying changes in network topology. Our approach incorporates Gradient-Descent based Model Predictive Control (GD-MPC), offering policy flexibility to accommodate prior knowledge and constraints. To address the intricacies of planning and overcome the high dimensionality inherent to such decision-making problems, we design an Amortize Network Interventions (ANI) framework, allowing for the pooling of optimal policies from history and other contexts, while ensuring a permutation equivalent property. This property enables efficient knowledge transfer and sharing across diverse contexts. Our approach has broad applications, from curbing infectious disease spread to reducing carbon emissions through traffic light optimization, and thus has the potential to address critical societal and environmental challenges

A class of pseudoinverse-free greedy block nonlinear Kaczmarz methods for nonlinear systems of equations

In this paper, we construct a class of nonlinear greedy average block Kaczmarz methods to solve nonlinear problems without computing the Moore-Penrose pseudoinverse. This kind of methods adopts the average technique of Gaussian Kaczmarz method and combines with the greedy strategy, which greatly reduces the amount of computation. The convergence analysis and numerical experiments of the proposed method are given. The numerical results show the effectiveness of the proposed methods

Dynamic behavior of a parasite–host model with general incidence

AbstractIn this paper, we consider the global dynamics of a microparasite model with more general incidences. For the model with the bilinear incidence, Ebert et al. [D. Ebert, M. Lipsitch, K.L. Mangin, The effect of parasites on host population density and extinction: Experimental epidemiology with Daphnia and six microparasites, American Naturalist 156 (2000) 459–477] observed that parasites can reduce host density, but the extinction of both host population and parasite population occurs only under stochastic perturbations. Hwang and Kuang [T.W. Hwang, Y. Kuang, Deterministic extinction effect of parasites on host populations, J. Math. Biol. 46 (2003) 17–30] studied the model with the standard incidence and found that the host population may be extinct in the absence of random disturbance. We consider more general incidences that characterize transitions from the bilinear incidence to the standard incidence to simulate behavior changes of populations from random mobility in a fixed area to the mobility with a fixed population density. Using the techniques of Xiao and Ruan [D. Xiao, S. Ruan, Global dynamics of a ratio-dependent predator–prey system, J. Math. Biol. 43 (2001) 268–290], it is shown that parasites can drive the host to extinction only by the standard incidence. The complete classifications of dynamical behaviors of the model are obtained by a qualitative analysis

SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning

In fisheye images, rich distinct distortion patterns are regularly distributed in the image plane. These distortion patterns are independent of the visual content and provide informative cues for rectification. To make the best of such rectification cues, we introduce SimFIR, a simple framework for fisheye image rectification based on self-supervised representation learning. Technically, we first split a fisheye image into multiple patches and extract their representations with a Vision Transformer (ViT). To learn fine-grained distortion representations, we then associate different image patches with their specific distortion patterns based on the fisheye model, and further subtly design an innovative unified distortion-aware pretext task for their learning. The transfer performance on the downstream rectification task is remarkably boosted, which verifies the effectiveness of the learned representations. Extensive experiments are conducted, and the quantitative and qualitative results demonstrate the superiority of our method over the state-of-the-art algorithms as well as its strong generalization ability on real-world fisheye images.Comment: Accepted to ICCV 202

A sketch-and-project method for solving the matrix equation AXB = C

In this paper, based on an optimization problem, a sketch-and-project method for solving the linear matrix equation AXB = C is proposed. We provide a thorough convergence analysis for the new method and derive a lower bound on the convergence rate and some convergence conditions including the case that the coefficient matrix is rank deficient. By varying three parameters in the new method and convergence theorems, the new method recovers an array of well-known algorithms and their convergence results. Meanwhile, with the use of Gaussian sampling, we can obtain the Gaussian global randomized Kaczmarz (GaussGRK) method which shows some advantages in solving the matrix equation AXB = C. Finally, numerical experiments are given to illustrate the effectiveness of recovered methods.Comment: arXiv admin note: text overlap with arXiv:1506.03296, arXiv:1612.06013, arXiv:2204.13920 by other author