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International Conference on Computer and Communications (ICCC)

作者： Zhibo Fan Hao Chu Fei Wang Jian Lu College of Information Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In recent years, node localization technology based on Wireless sensor network (WSN) plays an increasingly important role in production and life. The accuracy of node localization largely depends on the distance measurement between the target node and the beacon node. When there are many obstacles, the signal transmission is greatly disturbed, and the positioning accuracy is reduced. To solve this problem, a new non-line-of-sight (NLOS) localization method is proposed. Firstly, a statistical method based on Anderson-Darling (AD) test is employed to test the signal propagation state. After that, we construct the location function with constraints by LOS measurements and NLOS measurements. Next, we propose an improved particle swarm optimization (PSO) algorithm to determine the target location. Simulation results show that the proposed algorithm can effectively improve the positioning accuracy of the target node in complex environment.

关键词： Location awareness Wireless sensor networks Atmospheric measurements Statistical analysis Computational modeling Simulation Particle measurements

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S-GCN: A siamese spectral graph convolutions on brain connectivity networks 4

S-GCN: A siamese spectral graph convolutions on brain connec...

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4th International Symposium on Image Computing and Digital Medicine, ISICDM 2020

作者： Liu, Xiaoli Li, Jiali Cao, Peng Department of Chemical and Biomolecular Engineering Faculty of Engineering National University of Singapore Singapore Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781450389686

Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Network analysis provides a new way for exploring the association between brain functional deficits and the underlying structural disruption related to brain disorders. In this work we propose a Siamese GCN framework (called S-GCN) to learn useful representations for graph classification in an end-to-end fashion. To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Extensive experiments on ADNI dataset have demonstrated competitive performance of the S-GCN model. © 2020 ACM.

关键词： Neuroimaging

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A Novel Pretreatment Process for Vanadium Extraction: Purification and Recovery of Negative Component Calcium in Vanadium-Bearing Steel Slag

SSRN

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SSRN 2023年

作者： Ye, Guohua Zhang, Yun Xie, Yu Xiang, Xinyue Rong, Yiyang Song, Changxu Faculty of Land Resource Engineering Kunming University of Science and Technology Kunming650093 China State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization Kunming650093 China School of Resources and Civil Engineering Northeastern University Liaoning Shenyang110819 China

Vanadium-bearing steel slag, one kind of valuable metallurgical secondary resource, can be used as an important raw material for vanadium extraction, but its high calcium content seriously affects the index of vanadium extraction. Hence, a novel pretreatment process for vanadium extraction was proposed, that is, purification and recovery of negative component calcium in vanadium-bearing steel slag. This process included three closed-circuit procedures: Selective calcium leaching with no extra addition of fresh leaching reagent - Rapid and efficient precipitation of calcium - Cyclic leaching of calcium precipitation filtrate without supplement of leachant after simple regeneration. After pretreatment by the novel process, leaching efficiency and acid consumption of the following vanadium leaching procedure, were significantly increased and reduced respectively. Meanwhile, the evolution of phases before and after calcium leaching were investigated via XRD and SEM-EDS, showing that the selective calcium leaching was essentially the destruction and disappearance of calcium silicate phase, while the vanadium-phase was exposed after calcium leaching, thus facilitating the subsequent vanadium leaching. Moreover, the image analysis of Ca3SiO5-H2O-NH4Cl ternary system was performed, confirming that increasing temperature could promote calcium leaching reaction, and a stable product area could be formed through ammonium chloride reacting with tricalcium silicate. © 2023, The Authors. All rights reserved.

关键词： Slags

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Modeling Disease Progression with Deep Neural Networks 4

Modeling Disease Progression with Deep Neural Networks

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4th International Symposium on Image Computing and Digital Medicine, ISICDM 2020

ISBN: (纸本)9781450389686

Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. In this paper, we consider the problem of predicting disease progression measured by the cognitive scores and selecting biomarkers predictive of the progression. Most previous work on predicting AD progression ignore the issue of missing data. Missing data poses a major difficulty for modeling longitudinal data since most statistical models assume feature-complete data. We proposed and applied a minimal recurrent neural network model with skip to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We have performed extensive experiments to demonstrate the effectiveness of the proposed model using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). © 2020 ACM.

关键词： Neuroimaging

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Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective

arXiv

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arXiv 2024年

作者： Wu, Kai Pegoraro, Jacopo Meneghello, Francesca Zhang, J. Andrew Lacruz, Jesus O. Widmer, Joerg Restuccia, Francesco Rossi, Michele Huang, Xiaojing Zhang, Daqing Caire, Giuseppe Guo, Y. Jay SydneyNSW2007 Australia The University of Padova Italy The University of Padova Italy The School of Electrical and Data Engineering University of Technology Sydney Australia IMDEA Networks Spain IMDEA Madrid Spain Electrical and Computer Engineering Northeastern University United States The Department of Mathematics The University of Padova Italy Australia Peking University China IP Paris France The Faculty of Electrical Engineering and Computer Science The Technical University of Berlin Germany Australia

Integrated Sensing And Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far-separated transmitters and receivers. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Hence, to effectively enable ISAC, considerable research has been directed toward addressing the clock asynchronism issue in bi-static sensing. This paper provides an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC. © 2024, CC BY-NC-ND.

关键词： Clocks

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

arXiv

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arXiv 2024年

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Zhang, Lize Liu, Shuai Feng, Chaoyu Wang, Luyang Chen, Shuan Shao, Guangqi Wang, Xiaotao Lei, Lei Yang, Qirui Cheng, Qihua Xu, Zhiqiang Liu, Yihao Yue, Huanjing Yang, Jingyu Vasluianu, Florin-Alexandru Wu, Zongwei Ciubotariu, George Timofte, Radu Zhang, Zhao Zhao, Suiyi Wang, Bo Zuo, Zhichao Wei, Yanyan Teja, Kuppa Sai Sri Jayakar Reddy, A. Rongali, Girish Mitra, Kaushik Ma, Zhihao Liu, Yongxu Zhang, Wanying Shang, Wei He, Yuhong Peng, Long Yu, Zhongxin Luo, Shaofei Wang, Jian Miao, Yuqi Li, Baiang Wei, Gang Verma, Rakshank Maheshwari, Ritik Tekchandani, Rahul Hambarde, Praful Tazi, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Zhang, Haopeng Hou, Yingli Yao, Mingde Levin, M.S. Sundararajan, Aniruth Hari Kumar, A. Xiaomi Inc. China School of Electrical and Information Engineering Tianjin University China Shenzhen MicroBT Electronics Technology Co. Ltd China Shanghai Artificial Intelligence Laboratory China Computer Vision Lab CAIDAS IFI University of Würzburg Germany Laboratory for Multimedia Computing Hefei University of Technology China Detect Technologies Pvt Ltd India Indian Institute of Technology Madras India Xidian University China Harbin Institute of Technology China Northeastern University China University of Science and Technology of China China Fujian Normal University China Snap Inc. United States Tongji University China Hefei University of Technology China GEC Ajmer India CVPR Lab IIT Ropar India SCSS Trinity College Dublin Ireland Faculty of Robot Science and Engineering Northeastern University China Software College Northeastern University China The Chinese University of Hong Kong China Shiv Nadar University India

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024. Copyright © 2024, The Authors. All rights reserved.

关键词： Image sensors

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Unsupervised learning for monocular depth and motion with real scale

Unsupervised learning for monocular depth and motion with re...

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Chinese Automation Congress (CAC)

作者： Sibo Liu Lijin Fang College Information Science and Engineering Northeastern university Shenyang China Faculty of Robot Science and Engineering Northeastern university Shenyang China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In Computer Vision, the method of using a single image to learn depth information through the CNNs has gradually improved, especially for the unsupervised learning methods which trains without the ground-truth are based on the image process and vision. However, the depth map generated from these methods are relative depth values instead of absolute depths, therefore these methods can't be well applied to practical *** this paper, we use the monocular video sequence as input, and get partial true depth value by using a prior conditions, so as to constrain the depth and the relative pose of the entire sequence with the "real" scale. Here we solve the previous scale ambiguous problem by fusing the geometry information, which correct the scale of the entire sequence by our rescaled module. This paper only uses monocular sequences to accomplish these ***, our method is specifically effective in the field of automated guided vehicles with only a camera as the requirement which reducing the dependence on other devices but improved the quality.

关键词： Cameras Geometry Three-dimensional displays Vehicle dynamics Estimation robot sensing systems Image sequences

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High-Sensitivity Narrow‑Frequency T-Shaped Cantilever Fabry-Perot Acoustic Sensor for Photoacoustic Spectroscopy

SSRN

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SSRN 2024年

作者： Wang, Jilong Wang, Qiaoyun Yan, Chongyue Xu, Shunyuan Zou, Xin Wu, Qiang Ng, Wai Pang Fu, Yong-Qing Binns, Richard College of Information Science and Engineering Northeastern University at Qinhuangdao Liaoning Province Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Faculty of Engineering & Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom

The acoustic pressure detection is an important part in the PAS system. In this paper, an ultrahigh sensitivity Fabry-Perot acoustic sensor with a T-shaped cantilever was proposed. Simulation results showed that the sensitivity of such T-shaped cantilever was 1.5 times higher than that based on a rectangular cantilever, and the resonance frequency of T-shaped cantilever were able to modulate from 800 Hz to 1500 Hz by adjusting the multi-parameter characteristics. Experimental sensing results showed that the resonance frequency of T-shaped Fabry-Perot acoustic sensor was 1080 Hz, yielding a high sensitivity of 1.428 μm/Pa, with a signal-to-noise ratio (SNR) of 84.8 dB and a detectable pressure limit of 1.9 μPa/Hz1/2@1 kHz. We successfully used such acoustic sensor to measure acetylene (C2H2) concentration in the PAS. The sensitivity of PAS for C2H2 gas was 3.22 pm/ppm with a concentration range of 50 ppm ~100 ppm, and the minimum detection limit was 24.91ppb. © 2024, The Authors. All rights reserved.

关键词： Photoacoustic spectroscopy

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Fusion of Convolutional Neural Network with Xgboost Feature Extraction for Predicting Multi-Constituents in Corn Using Near Infrared Spectroscopy

SSRN

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SSRN 2024年

作者： Zou, Xin Wang, Qiaoyun Chen, Yinji Wang, Jilong Xu, Shunyuan Zhu, Ziheng Yan, Chongyue Shan, Peng Wang, Shuyu Fu, YongQing College of Information Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Faculty of Engineering & Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band, overlapping and non-specific nature. To solve these problems and extract implicit features from the raw data of NIR spectra and improve performance of quantitative models, a one-dimensional shallow convolutional neural network (CNN) model based on an XGBoost feature extraction method was proposed in this paper. The leaf node feature information in the XGBoost was encoded and reconstructed to obtain the implicit features of raw data in the NIR spectra. A two-parametric Swish (TSwish or TS) activation function was proposed to improve the performance of CNN, and the elastic net (EN) was also applied to avoid the overfitting problem of the CNN model. Performance of the developed XGBoost-CNN-TS-EN model was evaluated using two public NIR spectroscopy datasets of corn and soil, and the obtained determination coefficients (R2) for moisture, oil, protein, and starch of the corn were 0.993, 0.991, 0.998, and 0.992, respectively, with that of the soil organic matter being 0.992. The XGBoost-CNN-TS-EN model exhibits superior stability, good prediction accuracy, and generalization ability, demonstrating its great potentials for quantitative analysis of multi-constituents in spectroscopic applications. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heat...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： You Shen Yunzhou Zhang Yanmin Wu Zhenyu Wang Linghao Yang Sonya Coleman Dermot Kerr College of Information Science and Engineering Northeastern University Shenyang China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Computer Engineering Faculty of Robotics and Engineering North-eastern University Technical University in Munich Germany Shenyang China Department of Electronic and Computer Engineering Technical University Munich Germany School of Computing Engineering and Intelligent Systems Ulster University N. Ireland UK

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D.

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