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Voxel RCNN-HA: A Point Cloud Multiobject Detection Algorithm With Hybrid Anchors for Autonomous Driving

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第3期10卷 7286-7296页

作者： Wang, Hai Tao, Le Peng, Yiming Chen, Zhiyu Zhang, Yong Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Nanjing Forestry Univ Sch Automot & Traff Engn Nanjing 212001 Peoples R China

The 3-D object detection using Lidar becomes essential for subsequent vehicle decision-making and planning as part of an intelligent vehicle perception system. Voxel region convolutional neural network (RCNN) is a two-stage voxel-based 3-D object detection algorithm that is fast and accurate. However, the detection accuracy for specific categories is insufficient in complex traffic scenarios, and thus, we propose the Voxel RCNN-HA algorithm. First, in light of the shortcomings of Voxel RCNN in detecting pedestrians, a hybrid detection head is proposed to balance the advantages and disadvantages of anchor-based and anchor-free algorithms and significantly improve pedestrian detection performance while maintaining vehicle accuracy. Second, self-attention is introduced in the second stage of the algorithm and a Voxel region of interest (RoI) self-attention pooling module is developed to obtain both local and global features in RoI, which addresses the issue that the original Voxel RoI pooling module is challenging to obtain global features of large objects. On the one million scenes (ONCE) dataset, the proposed Voxel RCNN-HA achieves 66.79% mean average precision (mAP) and 11.7 frames per second (FPS), and outperforms both Voxel RCNN and CenterPoints in terms of detection accuracy. Additionally, experiments on the Waymo Open dataset and Custom-Rslidar dataset further validate the effectiveness and generalization of the proposed method.

关键词： Three-dimensional displays Proposals Prediction algorithms Pedestrians Feature extraction Laser radar inference algorithms 3-D object detection anchor-based anchor-free Lidar self-attention

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Joint Optimization of Measurement Point Intelligent Selection and End-to-End Network Traffic Calculation in Datacenters

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第3期11卷 2438-2449页

作者： Fan, Weibei Xiao, Fu Han, Lei He, Xin Wang, Junchang Nanjing Univ Posts & Telecommun Coll Comp Jiangsu High Technol Res Key Lab Wireless Sensor N Nanjing 210003 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Jilin 130012 Peoples R China

The effective design and management of data centers needs to follow the end-to-end traffic characteristics of data center networks (DCNs). However, directly measuring the end-to-end traffic of the network requires huge software and hardware costs. Since the particularity of the structure of DCNs, the flow estimation method used in the traditional computer network cannot be applied to existing DCNs. In this article, we study the end-to-end traffic calculation of cloud computing DCNs. We propose LLS-TC, which is an intelligent end-to-end traffic inference algorithm based on network tomography. Only using SNMP (simple network management protocol) data generally supported by switches, end-to-end traffic information can be calculated quickly and accurately. LLS-TC first devices a network traffic measurement point intelligent selection scheme based on node weighting. It first assigns weight to nodes through node criticality, and then uses node weighted incidence matrix approximation algorithm to calculate initial solution. LLS-TC then designs a network tomography method suitable for cloud computing network traffic calculation. It uses the time correlation of data center traffic to model the algorithm problem into a linear state space model, and finally calculates the traffic carried by each path through the improved Kalman filtering algorithm. Our evaluation and analysis demonstrate that LLS-TC can effectively use the extracted coarse-grained traffic characteristics, and greatly improve the accuracy of the calculation on the premise of ensuring the computational efficiency.

关键词： Data centers Cloud computing inference algorithms Feature extraction Costs Tomography Bayes methods network measurement measurement point selection end-to-end performance evaluation

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On the Feasible Region of Efficient algorithms for Attributed Graph Alignment

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第5期70卷 3622-3639页

作者： Wang, Ziao Zhang, Ning Wang, Weina Wang, Lele Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Univ Oxford Dept Stat Oxford OX1 3LB England Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA

Graph alignment aims at finding the vertex correspondence between two correlated graphs, a task that frequently occurs in graph mining applications such as social network analysis. Attributed graph alignment is a variant of graph alignment, in which publicly available side information or attributes are exploited to assist graph alignment. Existing studies on attributed graph alignment focus on either theoretical performance without computational constraints or empirical performance of efficient algorithms. This motivates us to investigate efficient algorithms with theoretical performance guarantee. In this paper, we propose two polynomial-time algorithms that exactly recover the vertex correspondence with high probability. The feasible region of the proposed algorithms is near optimal compared to the information-theoretic limits. When specialized to the seeded graph alignment problem under the seeded Erd & odblac;s-R & eacute;nyi graph pair model, the proposed algorithms extends the best known feasible region for exact alignment by polynomial-time algorithms.

关键词： Graph theory statistics inference algorithms

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BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第1期21卷 188-199页

作者： Xin, Junchang Wang, Mingcan Qu, Luxuan Chen, Qi Wang, Weiyiqi Wang, Zhiqiong Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Shenyang Key Lab Big Data Management & Analyt Liaoning Prov Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110819 Liaoning Peoples R China

Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ${Score}_{Lasso}$ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.

关键词： Bayes methods Linear regression Biological system modeling inference algorithms Biology Heuristic algorithms Correlation Gene regulatory network higher-order dynamic Bayesian network method fusion score function structure learning

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AB-VIP: Attack Method Using Brightness Information for Visibility Improvement of Adversarial Perturbations on the NMS Algorithm

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IEEE ACCESS 2025年 13卷 66065-66077页

作者： Kim, Gwang-Nam Lee, Han-Ju Lee, Han-Jin Jung, Hyeon-Jin Choi, Seok-Hwan Yonsei Univ Dept Comp Sci Wonju 26493 South Korea Yonsei Univ Div Software Wonju 26493 South Korea

As with classification models, object detection models are vulnerable to adversarial attacks. In particular, adversarial attacks on key components of object detection models such as Region Proposal Network (RPN) and Non-Maximum Suppression (NMS) algorithms have recently been highlighted. As a representative adversarial attack method targeting the NMS algorithm, PhantomSponges disrupts the normal operation of the NMS algorithm by using universal perturbations. However, since PhantomSponges focuses solely on the attack success rate, the adversarial examples it generates are visually noticeable. In this paper, we propose a simple yet effective attack method called Attack method using Brightness Information for Visibility Improvement(AB-VIP), to address the limitation of adversarial attack methods targeting the NMS algorithm. To improve the invisibility of adversarial examples, the proposed method adjusts universal perturbation using the brightness information of each target image. From the experimental results under various conditions, we demonstrate that the proposed method improves the invisibility of the existing NMS-targeted attack method while maintaining a high attack success rate. When applying the proposed method, the SSIM metric improved by 13.23%, while the L2 metric decreased by 66.23%.

关键词： Perturbation methods Object detection Brightness Proposals Classification algorithms Artificial neural networks Noise Measurement inference algorithms Image classification Adversarial attack cyber security NMS-algorithm object detection universal perturbation

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Structured Sparse Bayesian Learning Based Multiuser Detectors for Uplink Grant-Free NOMA With Variable User Activities

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 9093-9097页

作者： Zhang, Xiaoxu Fan, Pingzhi Li, Li Hao, Li Zhou, Ziyang Southwest Jiaotong Univ Prov Key Lab Informat Coding & Transmiss Chengdu 611756 Peoples R China

In this work, we study the grant-free non-orthogonal multiple access (GF-NOMA) system to support the massive machine-type communications (mMTC), where the number of users is large, but normally, only a small portion of them activate within a random access opportunity. For the uplink of this system, we develop a structured sparse Bayesian learning based multiuser detection (SSBL-MUD) scheme. In a practical mMTC scenario, the user activity status could change in different time slots. To solve the multiuser detection (MUD) problem, we formulate the uplink GF-NOMA in multiple time slots with variable user activity factors based on multiple measurement vector (MMV) model. In order to leverage the sparse structural feature of the transmitted signal, we propose multiple sparse Bayesian learning multiuser detection (MSBL-MUD) and block sparse Bayesian learning multiuser detection (BSBL-MUD) schemes. Both of them are able to recover the transmitted information by joint user activity detection and data detection, and do not require any prior knowledge of the user sparsity. Simulation results demonstrate that the proposed algorithms significantly outperform the existing classical detectors.

关键词： Bayes methods Uplink Multiuser detection Detectors Data models Matching pursuit algorithms inference algorithms Massive machine-type communications grant-free non-orthogonal multiple access multiple measurement vector structured sparse bayesian learning

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Quantized RIS-Aided mmWave Massive MIMO Channel Estimation With Uniform Planar Arrays

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第5期13卷 1230-1234页

作者： Wang, Ruizhe Ren, Hong Pan, Cunhua Jin, Shi Popovski, Petar Wang, Jiangzhou Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark Univ Kent Sch Engn Canterbury CT2 7NZ England

In this letter, we investigate a cascaded channel estimation method for a millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system aided by a reconfigurable intelligent surface (RIS) with the base station (BS) equipped with low-resolution analog-to-digital converters (ADCs), where the BS and the RIS are both equipped with a uniform planar array (UPA). Due to the sparse property of mmWave channel, the channel estimation can be solved as a compressed sensing (CS) problem. However, the low-resolution quantization cause severe information loss of signals, and traditional CS algorithms are unable to work well. To recovery the signal and the sparse angular domain channel from quantization, we introduce Bayesian inference and efficient vector approximate message passing (VAMP) algorithm to solve the quantize output CS problem. To further improve the efficiency of the VAMP algorithm, a Fast Fourier Transform (FFT) based fast computation method is derived. Simulation results demonstrate the effectiveness and the accuracy of the proposed cascaded channel estimation method for the RIS-aided mmWave massive MIMO system with few-bit ADCs. Furthermore, the proposed channel estimation method can reach an acceptable performance gap between the low-resolution ADCs and the infinite ADCs for the low signal-to-noise ratio (SNR), which implies the applicability of few-bit ADCs in practice.

关键词： Millimeter wave communication Channel estimation Quantization (signal) Massive MIMO Estimation Sparse matrices inference algorithms Low-resolution analog-to-digital converter channel estimation millimeter wave reconfigurable intelligent surface approximate message passing

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Energy-Saving Clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy inference

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IEEE SENSORS JOURNAL 2022年第3期22卷 2845-2857页

作者： Hou, Jun Qiao, Jianhua Han, Xinglong Taiyuan Univ Sci & Technol Elect Informat Engn Dept Taiyuan 030024 Peoples R China

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy Clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, Clustering Type1 and Clustering Type2, and alternately executes them in different rounds according to the threshold. Clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. Clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others.

关键词： Clustering algorithms Heuristic algorithms inference algorithms Wireless sensor networks Sensors Fuzzy logic Base stations Cluster routing protocol energy-saving routing algorithm fuzzy inference system wireless sensor network

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Privacy-Preserving Algorithm for APPs in Vehicle Intelligent Terminal System: A Compressive Method

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 17352-17365页

作者： Gao, Chenlu Lu, Jianquan Lou, Jungang Liu, Yang Yu, Wenwu Southeast Univ Sch Cyber Sci Engn Nanjing 210096 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Huzhou Univ Yangtze Delta Reg Huzhou Inst Intelligent Transpor Huzhou 313000 Peoples R China Zhejiang Normal Univ Sch Math Sci Key Lab Intelligent Educ Technol & Applicat Zhejia Jinhua 321004 Peoples R China Purple Mt Labs Nanjing 211102 Peoples R China

Vehicle intelligent terminals often require end-user applications (APPs) to continuously send information to external data aggregator for monitoring or control tasks. However, communication channels exposed to the open environment can lead to an undesirable loss of privacy for drivers, despite the benefits provided by these APPs. It is worth mentioning that different APPs of vehicle intelligent terminals may require different key information. Given these personalized application scenarios, the use of traditional privacy protection technology may encounter limitations. To address this problem, we propose a compressive privacy-preserving algorithm that employs a compression matrix to maintain the normal function of applications while ensuring privacy. Specifically, the algorithm utilizes sensors to linearly transform the original vectors of measurements at each time step into a lower-dimensional space. The compressed measurements are then transmitted to a fusion center. Optimization problems are formulated for the current time step as well as two time steps. The performance of the algorithm is evaluated using the Cramer-Rao bound. Simulation results and comparisons further validate the effectiveness of the compressive privacy algorithm.

关键词： Privacy Data privacy Vectors inference algorithms Optimization Vehicle dynamics Time measurement Vehicle intelligent terminals applications key information personalized application scenarios compressive privacy-preserving

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ATWR-SMR: An Area-Constrained Truthful-Worker Recruitment-Based Sensing Map Recovery Scheme for Sparse MCS in Extreme-Environment Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 3711-3724页

作者： Fu, Xiangwan Liu, Anfeng Xiong, Neal N. Wang, Tian Zhang, Shaobo Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Sul Ross State Univ Dept Comp Sci & Math Alpine TX 79830 USA Beijing Normal Univ Artificial Intelligence & Future Networks Zhuhai 519000 Guangdong Peoples R China UIC Zhuhai 519087 Guangdong Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Hunan Peoples R China

Sparse mobile crowdsensing (SMCS) is a prospective solution for large-scale data sensing through mobile devices of Internet of Things (IoT) systems where IoT systems cannot obtain the sensing data of the area under extreme environments. The unsensed area data can be obtained by the data inference algorithm trained by the sensed data of recruited workers. However, recruited workers may upload false data in exchange for payment, and the platform is unable to distinguish between true and false data. In this article, our goal is to maximize the SMCS platform's total profit, where the platform cannot verify the authenticity of the sensed data, and the requester's payment is based on the sensing task's data quality. To meet the objective, we propose the area-constrained truthful worker recruitment-based sensing map recovery (ATWR-SMR) scheme, which includes the area constraint, the area-constrained truthful worker recruitment, and the sensing map recovery. 1) The area constraint establishes the importance of areas by history data differences in the sliding window. 2) The truthful worker recruitment identifies trustworthy workers by the truthful upper confidence bound algorithm and recruits low-cost trustworthy workers to sense high-importance areas. 3) The sensing map recovery infers the unsensed data by the deep matrix factorization algorithm trained by the history truthful data set. Finally, we verify the effectiveness of the ATWR-SMR scheme in improving the total profit of the platform through extensive comparison experiments based on the China air quality data set.

关键词： Sensors History Internet of Things Recruitment inference algorithms Crowdsensing Costs Internet of Things (IoT) sensing map recovery sparse mobile crowdsensing (SMCS) truthful worker recruitment upper confidence bound algorithm

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