A Generic Plug-and-Play Navigation Fusion Strategy for Land Vehicles in GNSS-Denied Environment

Achieving accurate navigation information by integrating multiple sensors is key to the safe operation of land vehicles in global navigation satellite system(GNSS)-denied environment.However,current multi-sensor fusion methods are based on stovepipe architecture,which is optimized with custom fusion strategy for specific sensors.Seeking to develop adaptable navigation that allows rapid integration of any combination of sensors to obtain robust and high-precision navigation solutions in GNSS-denied environment,we propose a generic plug-and-play fusion strategy to estimate land vehicle states.The proposed strategy can handle different sensors in a plug-and-play manner as sensors are abstracted and represented by generic models,which allows rapid reconfiguration whenever a sensor signal is additional or lost during operation.Relative estimations are fused with absolute sensors based on improved factor graph,which includes sensors’error parameters in the non-linear optimization process to conduct sensor online calibration.We evaluate the performance of our approach using a land vehicle equipped with a global positioning system(GPS)receiver as well as inertial measurement unit(IMU),camera,wireless sensor and odometer.GPS is not integrated into the system but treated as ground truth.Results are compared with the most common filtering-based fusion algorithm.It shows that our strategy can process low-quality input sources in a plug-and-play and robust manner and its performance outperforms filtering-based method in GNSS-denied environment.

Plug-and-play dual-phase-modulated continuous-variable quantum key distribution with photon subtraction

Plug-and-play dual-phase-modulated continuous-variable quantum key distribution (CVQKD) protocol can effectively solve the security loopholes associated with transmitting local oscillator (LO). However, this protocol has larger excess noise compared with one-way Gaussian-modulated coherent-states scheme, which limits the maximal transmission distance to a certain degree. In this paper, we show a reliable solution for this problem by employing non-Gaussian operation, especially, photon subtraction operation, which provides a way to improve the performance of plug-and-play dual-phase-modulated CVQKD protocol. The photon subtraction operation shows experimental feasibility in the plug-andplay configuration since it can be implemented under current technology. Security results indicate that the photon subtraction operation can evidently enhance the maximal transmission distance of the plug-and-play dual-phase-modulated CVQKD protocol, which effectively makes up the drawback of the original one. Furthermore, we achieve the tighter bound of the transmission distance by considering the finite-size effect, which is more practical compared with that achieved in the asymptotic limit.

Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation

Different from a general density estimation,the crime density estimation usually has one important factor:the geographical constraint.In this paper,a new crime density estimation model is formulated,in which the regions where crime is impossible to happen,such as mountains and lakes,are excluded.To further optimize the estimation method,a learning-based algorithm,named Plug-and-Play,is implanted into the augmented Lagrangian scheme,which involves an off-the-shelf filtering operator.Different selections of the filtering operator make the algorithm correspond to several classical estimation models.Therefore,the proposed Plug-and-Play optimization based estimation algorithm can be regarded as the extended version and general form of several classical methods.In the experiment part,synthetic examples with different invalid regions and samples of various distributions are first tested.Then under complex geographic constraints,we apply the proposed method with a real crime dataset to recover the density estimation.The state-of-the-art results show the feasibility of the proposed model.

Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging With Convergence Analysis

Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.

Interactive Transformer for Small Object Detection

The detection of large-scale objects has achieved high accuracy,but due to the low peak signal to noise ratio(PSNR),fewer distinguishing features,and ease of being occluded by the surroundings,the detection of small objects,however,does not enjoy similar success.Endeavor to solve the problem,this paper proposes an attention mechanism based on cross-Key values.Based on the traditional transformer,this paper first improves the feature processing with the convolution module,effectively maintaining the local semantic context in the middle layer,and significantly reducing the number of parameters of the model.Then,to enhance the effectiveness of the attention mask,two Key values are calculated simultaneously along Query and Value by using the method of dual-branch parallel processing,which is used to strengthen the attention acquisition mode and improve the coupling of key information.Finally,focusing on the feature maps of different channels,the multi-head attention mechanism is applied to the channel attention mask to improve the feature utilization effect of the middle layer.By comparing three small object datasets,the plug-and-play interactive transformer(IT-transformer)module designed by us effectively improves the detection results of the baseline.

Learning to optimize:A tutorial for continuous and mixed-integer optimization

Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimization problems frequently share common structures,L2O provides a tool to exploit these structures for better or faster solutions.This tutorial dives deep into L2O techniques,introducing how to accelerate optimization algorithms,promptly estimate the solutions,or even reshape the optimization problem itself,making it more adaptive to real-world applications.By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand,this tutorial provides a comprehensive guide for practitioners and researchers alike.

Domestic PV System with Feedback Linearization-based Control Strategy for Module-level MPPT under Partial Shading Condition

A partial shading condition can adversely affect the energy conversion efficiency of domestic photovoltaic(PV) systems. Connecting each PV module to a microinverter and performing module-level maximum power point tracking(MPPT)are proposed as promising solutions. In this paper, a feedback linearization-based control strategy is designed for the nonlinear system by a novel straightforward approach. The obtained nonlinear control law can independently govern each microinverter, providing module-level MPPT for PV modules without DC optimizer. Moreover, PV modules can be easily connected or disconnected due to the lug-and-play ability of the proposed controller. As a result, the proposed PV system can be easily maintained and extended even by non-expert users. Moreover,any module failure in the proposed PV system can be tolerated without impacts on the normal operation of other PV modules.The advantages of the proposed control strategy are verified by the simulation of a test PV system in MATLAB/Simulink under various partial shading conditions as well as adding or removing PV modules.