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Kalman Filter-based Sensing in Communication Systems with Clock Asynchronism

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arXiv 2022年

作者： Chen, Xu Feng, Zhiyong Zhang, J. Andrew Yuan, Xin Zhang, Ping School of Information and communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Global Big Data Technologies Centre University of Technology Sydney SydneyNSW2007 Australia The Data61 Commonwealth Scientific and Industrial Research Organization SydneyNSW2122 Australia State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset (CFO), leading to major challenges in uplink sensing. Unlike existing technologies, our scheme does not require knowing the location of the UE in advance, and retains the linearity of the sensing parameter estimation problem. We first estimate the angle-of-arrivals (AoAs) of multipaths and use them to spatially filter the CSI. Then, we propose a KF-based CSI enhancer that exploits the estimation of Doppler with CFO as the prior information to significantly suppress the time-varying noise-like TO terms in spatially filtered CSIs. Subsequently, we can estimate the accurate ranges of UE and the scatterers based on the KF-enhanced CSI. Finally, we identify the UE's AoA and range estimation and locate UE, then locate the dumb scatterers using the bi-static system. Simulation results validate the proposed scheme. The localization root mean square error of the proposed method is about 20 dB lower than the benchmarking scheme. © 2022, CC BY-NC-SA.

关键词： Kalman filters

A Traversal Multi-Target Path Planning Method for Multi-Unmanned Surface Vessels in Space-Varying Ocean Current

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SSRN

SSRN 2022年

作者： Yu, Jiabin Chen, Zhihao Zhao, Zhiyao Yao, Peng Xu, Jiping Artificial Intelligence Beijing Technology and Business University Beijing100048 China China and Beijing Laboratory for Intelligent Environmental Protection Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China College of Engineering Ocean University of China Qingdao266100 China

The multi-USVs cooperation is widely used. Multi-target traversing is an important aspect on the research on the multi-USVs cooperation in space-varying ocean current environment. Therefore, this paper proposes a traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current. The proposed algorithm is divided into two steps: First, a task allocation method for multi-USVs traveling multi-target based on improved Hungary algorithm is proposed for multi-target task allocation. The improved Hungary algorithm is used to allocate tasks of multi-target. Second, to solve the path planning problem, a virtual field RRT* (VF-RRT*) algorithm is proposed. The virtual field sampling algorithm and the ocean current constraint function are introduced in the RRT* algorithm. In order to verify the effectiveness of the algorithm, three groups of simulation experiments are implemented. The results show that the proposed algorithm has good effectiveness in path planning and task allocation. © 2022, The Authors. All rights reserved.

关键词： Motion planning

Natural image reconstruction from fMRI using deep learning: A survey

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arXiv 2021年

作者： Rakhimberdina, Zarina Jodelet, Quentin Liu, Xin Murata, Tsuyoshi Department of Computer Science Tokyo Institute of Technology Tokyo152-8552 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo135-0064 Japan AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory Tokyo152-8550 Japan

With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain. One of the most challenging brain decoding tasks is the accurate reconstruction of the perceived natural images from brain activities measured by functional magnetic resonance imaging (fMRI). In this work, we survey the most recent deep learning methods for natural image reconstruction from fMRI. We examine these methods in terms of architectural design, benchmark datasets, and evaluation metrics and present a fair performance evaluation across standardized evaluation metrics. Finally, we discuss the strengths and limitations of existing studies and present potential future directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

A Traversal Multi-Target Path Planning Method for Multi-Unmanned Surface Vessels in Space-Varying Ocean Current

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SSRN

SSRN 2023年

The multi-unmanned surface vessels (USVs) cooperation is widely used. Multi-target traversing is an important aspect on the research on the multi-USVs cooperation in space-varying ocean current environment. Therefore, this paper proposes a traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current. The proposed algorithm is divided into two steps: First, a task allocation method for multi-USVs traveling multi-target based on improved Hungary algorithm is proposed for multi-target task allocation. The improved Hungary algorithm is used to allocate tasks of multi-target. Second, to solve the path planning problem, a virtual field RRT* (VF-RRT*) algorithm is proposed. The virtual field sampling algorithm and the ocean current constraint function are introduced in the RRT* algorithm. In order to verify the effectiveness of the algorithm, three groups of simulation experiments are implemented. The results show that the proposed algorithm has good effectiveness in path planning and task allocation. © 2023, The Authors. All rights reserved.

关键词： Motion planning

Implementation of improved RGBD 3D target detection model based on FPGA heterogeneous computing architecture

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Implementation of improved RGBD 3D target detection model ba...

第34届中国控制与决策会议

作者： Yu WANG Wenbin FENG Chongchong YU Xinyu HU Yuqiu ZHANG China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University School of artificial intelligence Beijing Technology and Business University State Key Laboratory of Coal Mine Safety Technology China Coal Technology & Engineering Group Shenyang Research Institute Oil production Process Research Institute Oil Production Plant 3 of PetroChina Changqing Oilfield Company

In order to solve the problems of low model accuracy,poor computing power,poor parallel ability and excessive power consumption in the deployment of RGBD based 3 D target detection model at the embedded end,this paper first proposes an improved RGBD 3 D target detection model based on ENet semantic segmentation model,which takes ENet as the semantic segmentation network,RGB image and depth information are fused to realize 3 D target ***,in order to apply the model at the edge,this paper constructs a lightweight network and cuts the network in the down-sampling stage of ENet ***,this paper uses Xilinx ZCU104 as the hardware development kit,which takes FPGA as the auxiliary parallel operation unit and ARM as the main operation *** is a heterogeneous computing architecture with the ability to deal with complex *** architecture uses FPGA to accelerate the depth model in parallel,which improves the operation speed and reduces the power *** test results of the model on ZCU104 are compared with other *** results show that while ensuring the accuracy,the power consumption of the heterogeneous computing architecture used in this paper is 93% lowerthan that of Intel Xeon e5-2620 v4 CPU,the speed is 12 times higher,and the speed is more than 180 times higher than that of ARM Cortex-A53 commonly used at the edge.

关键词： FPGA parallel acceleration Heterogeneous computing architecture 3D target detection

LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices

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arXiv 2024年

作者： Ding, Chuntao Cao, Xu Xie, Jianhang Fan, Linlin Wang, Shangguang Lu, Zhichao School of Artificial Intelligence Beijing Normal University Beijing China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education with School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer and Information Engineering Henan Normal University Henan453007 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science City University of Hong Kong HKSAR Hong Kong

Efficient fine-tuning of pre-trained convolutional neural network (CNN) models using local data is essential for providing high-quality services to users using ubiquitous and resource-limited Internet of Things (IoT) devices. Low-Rank Adaptation (LoRA) fine-tuning has attracted widespread attention from industry and academia because it is simple, efficient, and does not incur any additional reasoning burden. However, most of the existing advanced methods use LoRA to fine-tune Transformer, and there are few studies on using LoRA to fine-tune CNN. The CNN model is widely deployed on IoT devices for application due to its advantages in comprehensive resource occupancy and performance. Moreover, IoT devices are widely deployed outdoors and usually process data affected by the environment (such as fog, snow, rain, etc.). The goal of this paper is to use LoRA technology to efficiently improve the robustness of the CNN model. To this end, this paper first proposes a strong, robust CNN fine-tuning method for IoT devices, LoRA-C, which performs low-rank decomposition in convolutional layers rather than kernel units to reduce the number of fine-tuning parameters. Then, this paper analyzes two different rank settings in detail and observes that the best performance is usually achieved when α/r is a constant in either standard data or corrupted data. This discovery provides experience for the widespread application of LoRA-C. Finally, this paper conducts many experiments based on pre-trained models. Experimental results on CIFAR-10, CIFAR-100, CIFAR-10-C, and Icons50 datasets show that the proposed LoRA-Cs outperforms standard ResNets. Specifically, on the CIFAR-10-C dataset, the accuracy of LoRA-C-ResNet-101 achieves 83.44% accuracy, surpassing the standard ResNet-101 result by +9.5%. On the Icons-50 dataset, the accuracy of LoRA-C-ResNet-34 achieves 96.9% accuracy, surpassing the standard ResNet-34 result by +8.48%. In addition, compared with full parameter fine-tuning, LoRA-

关键词： Quality of service

data placement for multi-tenant data federation on the cloud

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arXiv 2021年

作者： Liu, Ji Mo, Lei Yang, Sijia Zhou, Jingbo Ji, Shilei Xiong, Haoyi Dou, Dejing Big Data Lab Baidu Inc. Beijing China School of Automation Southeast University Nanjing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Business Intelligence Lab Baidu Inc. Beijing China Security department Baidu Inc. Beijing China

Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. data federation brings new opportunities to the data-related cooperation among organizations by providing abstract data interfaces. With the development of cloud computing, organizations store data on the cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either execution time or monetary cost, and do not consider data partitioning for hard constraints. In this paper, we propose an approach to enable data processing on the cloud with the data from different organizations. The approach consists of a data federation platform named FedCube and a Lyapunov-based data placement algorithm. FedCube enables data processing on the cloud. We use the data placement algorithm to create a plan in order to partition and store data on the cloud so as to achieve multiple objectives while satisfying the constraints based on a multi-objective cost model. The cost model is composed of two objectives, i.e., reducing monetary cost and execution time. We present an experimental evaluation to show our proposed algorithm significantly reduces the total cost (up to 69.8%) compared with existing approaches. © 2021, CC BY.

关键词： Cloud computing

Generalized quantum teleportation of shared quantum secret with quantum walks

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arXiv 2020年

作者： Li, Hengji Li, Jian Chen, Xiubo Information Security Center State Key Laboratory Networking and Switching Technology Beijing University of Posts Telecommunications Beijing100876 China School of Artificial Intelligence Beijing University of Posts Telecommunications Beijing100876 China Center for Quantum Information Research ZaoZhuang University ZaoZhuang Shandong277160 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data GuiyangGuizhou550025 China

Very recently, Lee et al. proposed the first secure quantum teleporation protocol, where quantum information shared by an arbitrary number of senders can be transferred to another arbitrary number of receivers. Here, by introducing quantum walks, a novel secure (n, m) quantum teleportation of shared quantum secret between n senders and m receivers is presented. Firstly, two kinds of (n, 2) teleportation schemes are proposed by n-walker quantum walks on the line, the first walker of which is driven by three coins, respectively, based on two kinds of coin operators: the homogeneous coins and the position-dependent coins. Secondly, by increasing the amount of the coins of the first walker to m + 1, the previous (n, 2) scheme can be generalized to (n, m) teleportation scheme. Then, we give the proof of the information security of our proposed scheme, in which neither any single nor subparties of senders and receivers can fully access the secret quantum information. Moreover, the projective measurements are needed, instead of the joint Bell measurements that are necessary in Lee et al.’s protocol. Our work can also be extended further to QWs on the cycle. This work provides an additional relevant instance of the richness of quantum walks for quantum information processing tasks and thus opens the wider application purpose of quantum walks. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

Implementation of improved RGBD 3D target detection model based on FPGA heterogeneous computing architecture

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Implementation of improved RGBD 3D target detection model ba...

Chinese Control and Decision Conference, CCDC

作者： Yu Wang Wenbin Feng Chongchong Yu Xinyu Hu Yuqiu Zhang China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University Beijing China School of Artificial Intelligence Beijing Technology and Business University Beijing China State Key Laboratory of Coal Mine Safety Technology China Coal Technology&Engineering Group Shenyang Research Institute China Oil production Process Research Institute Oil Production Plant 3 of PetroChina Changqing Oilfield Company Yinchuan China

ISBN: (纸本)9781665478977

In order to solve the problems of low model accuracy, poor computing power, poor parallel ability and excessive power consumption in the deployment of RGBD based 3D target detection model at the embedded end, this paper first proposes an improved RGBD 3D target detection model based on ENet semantic segmentation model, which takes ENet as the semantic segmentation network, RGB image and depth information are fused to realize 3D target detection. Secondly, in order to apply the model at the edge, this paper constructs a lightweight network and cuts the network in the down-sampling stage of ENet model. Finally, this paper uses Xilinx ZCU104 as the hardware development kit, which takes FPGA as the auxiliary parallel operation unit and ARM as the main operation unit. It is a heterogeneous computing architecture with the ability to deal with complex operations. The architecture uses FPGA to accelerate the depth model in parallel, which improves the operation speed and reduces the power consumption. The test results of the model on ZCU104 are compared with other hardware. The results show that while ensuring the accuracy, the power consumption of the heterogeneous computing architecture used in this paper is 93% lower than that of Intel Xeon e5-2620 v4 CPU, the speed is 12 times higher, and the speed is more than 180 times higher than that of ARM Cortex-A53 commonly used at the edge.

关键词： Solid modeling Three-dimensional displays Power demand Computational modeling Semantic segmentation Process control Object detection