查看更多>>摘要：The development of a distributed trajectory-tracking control strategy that is independent of velocity measurements is critical to achieving finite-time tracking control of autonomous underwater vehicle(AUV)systems.In this study,a group of heterogeneous AUV systems with intermittent communication links is considered and a finite-time trajectory-tracking control strategy is developed.The strategy includes two observers and one controller proposed for each follower-AUV.The first observer,a hybrid finite-time observer,estimates the state of the leader,whereas the second observer,which relies only on the position measurement,is proposed to estimate the states of the follower-AUV itself.In addition,a distributed trajectory-tracking controller is designed using the states estimated by the intermittent communication network even without velocity measurements.A homogeneous technique is utilized to prove that all followers can track the leader in a finite time.Finally,the effectiveness of the developed finite-time tracking control strategy is illustrated by numerical simulations.
查看更多>>摘要：This paper deals with a recursive filtering problem for a class of discrete time-varying nonlinear networked systems with the encoding-decoding mechanism.The linear fitting method is introduced to handle the nonlinearity.An encoding-decoding mechanism is constructed to describe the data transmission process in wireless communication networks(WCNs).To be specific,the measurement outputs are mapped by a quantizer to unique codewords for transmission in WCNs.Then,the codewords are decoded by the decoder to recover the measurement outputs which are sent to the filter.The processing/encoding delay and network delay have been considered.Firstly,on the premise that the upper bound of the filtering error covariance is minimum,the appropriate filtering gain is calculated.Then,the mean square exponential boundedness of the filtering error is analyzed.Finally,two simulation examples are presented to verify the effectiveness of the proposed algorithm.
查看更多>>摘要：Autonomous robots have garnered extensive utilization in diverse fields.Among the critical con-cerns for autonomous systems,path planning holds paramount importance.Notwithstanding considerable efforts in its development over the years,path planning for autonomous systems continues to grapple with challenges related to low planning efficiency and inadequate obstacle avoidance response in a timely manner.This study proposes a novel and systematic solution to the path planning problem within intricate office buildings.The solution consists of a global planner and a local planner.To handle the global planning aspect,an adaptive clustering-based dynamic programming rapidly exploring random tree(ACDP-RRT)al-gorithm is proposed.ACDP-RRT effectively identifies obstacles on the map by leveraging geometric features.These obstacles are then represented as a collection of sequentially arranged convex polygons,optimizing the sampling region and significantly enhancing sampling efficiency.For local planning,a network decoupling actor-critic(ND-AC)algorithm is employed.The proposed ND-AC simplifies the local planner design process by integrating planning and control loops into a neural network(NN)trained via an end-to-end model-free deep reinforcement learning(DRL)framework.Moreover,the adoption of network decoupling(ND)tech-niques leads to an improved obstacle avoidance success rate when compared to conventional actor-critic(AC)-based methods.Extensive simulations and experiments are conducted to demonstrate the effectiveness and robustness of the proposed approach.
查看更多>>摘要：Hybrid orbital networking and the proliferation of satellites raise explosive management com-plexity in mega-satellite networks(MSNs)and prompt the management architecture to the hierarchy.In this regard,we propose a tiered clustering-based management architecture(TCMA)to realize the high-efficient and low-cost management performance.Specifically,based on the constrained satellite coverage,we derive an expression of the mapping relationship between the required control overhead of the controller interaction,the average path length of the proposed TCMA,and the corresponding number of management tiers and cluster size.The expression can determine the optimal number of tiers of the TCMA by minimizing the required flow table size between controllers,while ensuring high transmission efficiency.We then exhibit the elasticity of the hierarchical TCMA by adding the peer connections at the second-highest management tier to reduce maximum redundant paths.Simulation results verify the effectiveness of the proposed TCMA and illustrate that a 3-tier TCMA is sufficient for thousands of satellites.
查看更多>>摘要：As wireless communication traffic experiences rapid growth,the carbon emissions caused by the communication industry are also on the rise.To achieve"carbon neutrality",researchers are considering the use of renewable energy sources to power cellular networks,thereby reducing carbon emissions.However,a challenge arises when using renewable energy,specifically owing to the unpredictable nature of both the energy consumption of the cellular network and the power generation from renewable sources.This inconsistency results in low renewable energy utilization and reduced carbon efficiency.Herein,we construct a carbon efficiency model of solar-powered cellular networks using practical data from solar radiation.We propose a mechanism that alternately optimizes the performance of the renewable energy network and the cellular network.This approach is based on convex optimization theory and the Dinkelback algorithm,and it leads to the design of a carbon efficiency optimization algorithm.This algorithm aims to improve the carbon efficiency of cellular networks and reduce their carbon emissions.Simulation results demonstrate that our optimization scheme yields a maximum improvement of 2.56 x 108 bps/g in the carbon efficiency of the cellular network as compared to conventional power allocation schemes such as the traditional water filling method and heuristic energy sharing and charge/discharge algorithms.
查看更多>>摘要：Next-generation wireless network aims to support low-latency,high-speed data transmission services by incorporating artificial intelligence(AI)technologies.To fulfill this promise,AI-based network traffic prediction is essential for pre-allocating resources,such as bandwidth and computing power.This can help reduce network congestion and improve the quality of service(QoS)for users.Most studies achieve future traffic prediction by exploiting deep learning and reinforcement learning,to mine spatio-temporal correlated variables.Nevertheless,the prediction results obtained only by the spatio-temporal correlated variables cannot reflect real traffic changes.This phenomenon prevents the true prediction variables from being inferred,making the prediction algorithm perform poorly.Inspired by causal science,we propose a novel network traffic prediction method based on self-supervised spatio-temporal causal discovery(SSTCD).We first introduce the Granger causal discovery algorithm to build a causal graph among prediction variables and obtain spatio-temporal causality in the observed data,which reflects the real reasons affecting traffic changes.Next,a graph neural network(GNN)is adopted to incorporate causality for traffic prediction.Furthermore,we propose a self-supervised method to implement causal discovery to to address the challenge of lacking ground-truth causal graphs in the observed data.Experimental results demonstrate the effectiveness of the SSTCD method.
查看更多>>摘要：In this paper,we investigated the effects of error-feedback noise shaping(EFNS)on a low-resolution Kramers-Kronig(KK)detection system for the first time.Both 16-ary quadrature amplitude modulation(16-QAM)and 64-ary quadrature amplitude modulation(64-QAM)were considered in the ex-periment.The carrier tone was added on the transmitter side using the virtual carrier-assisted single sideband(VCA-SSB)method to meet the minimum phase(MP)condition.Under the MP condition,KK detection only needs the intensity of the optical signal to be detected by one photodiode(PD)to retrieve the phase information.However,the virtual carrier tone increases the carrier-to-signal power ratio(CSPR),which results in a high peak-to-average power ratio(PAPR)at the transmitter,causing an increase in quantization noise.To mitigate quantization noise in the low-resolution KK detection system,EFNS was applied.The application of EFNS increased the optimal CSPR of the KK detection system and improved receiver sensi-tivity.Using a 4-bit DAC and the EFNS,80 km transmission was achieved in the 60-Gbit/s 64-QAM KK detection system.
查看更多>>摘要：The identification of prototypical waveforms,such as sleep spindles and epileptic spikes,is crucial for the diagnosis of neurological disorders.These prototypical waveforms are usually recurrently presented in certain brain states,serving as potential biomarkers for clinical evaluations.Convolutional sparse coding(CSC)approaches have demonstrated strength in identifying recurrent patterns in time-series.However,existing CSC approaches do not explicitly explore state-specific patterns,making it difficult to identify state-related biomarkers.To address this problem,we propose state-sensitive CSC to learn state-specific prototypical waveforms.Specifically,we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states.Based on this,state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations.Experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states,providing potential biomarkers for seizure prediction.Our approach provides a promising tool for automatic biomarker candidate identification.
查看更多>>摘要：In-memory computing(IMC),leveraging emerging memories,holds significant promise in over-coming memory limitations and improving energy efficiency.However,the prevailing IMC array structure based on serially connected transistors and memory cells(1T1R/2T2R),along with the signed weight map-ping scheme,can lead to asymmetrical weight sensing issues(AWS)due to electrical asymmetry within the 1T1R/2T2R structure,particularly in highly scaled cells where the transistor's resistance becomes signifi-cant.In this paper,we propose and fabricate an electrically symmetric memory cell based on a physically isolated 2T2R structure for IMC.This design aims to enhance the precision and density of RRAM-based IMC arrays.The 2T2R cells are manufactured using the back-end-of-line(BEOL)process of a commercial 40 nm technology platform.The feasibility of this design is verified through measured and simulated results,showcasing its capability to address the issue of AWS.Compared to conventional 2T2R cells,this design achieves a considerably smaller transistor footprint without compromising accuracy,while also improving integration density by 42.2％.These innovative memory cell advancements have the potential to further advance high-energy-efficient IMC technology.
查看更多>>摘要：In the devices that integrate phase change memory(PCM)and ovonic threshold switching(OTS),the OTS threshold voltage often surpasses the RESET operation voltage of PCM.The conventional applica-tion of square pulses hinders the successful completion of the SET operation in these integrated devices.To address this challenge,a novel pulse called the surge pulse is introduced,which comprises a high amplitude pulse for OTS activation and a low amplitude pulse for PCM operation.By employing COMSOL simulation,the operational effectiveness of both square pulses and surge pulses is validated.Test results reveal that using a square pulse to operate the integrated device accelerates the occurrence of SET-stuck failure(SSF).In contrast,the surge pulse enables the integrated device to operate for at least 1000 cycles while preserving the essential cyclic characteristics.Additionally,an investigation into the overshoot component of the surge pulse is conducted,revealing that an increase in overshoot amplitude and pulse width also accelerates the emergence of SSF.By applying the theory of ion migration induced by the electric field,the root cause of SSF in integrated devices is explained,and the accuracy of the theory is validated through the application of a reverse pulse.In summary,this study elucidates the rationality of replacing the square pulse with a surge pulse,presenting a superior approach for operating PCM and OTS integrated devices.
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