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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Xiang, Rong Liu, Yuan Zhang, Qieshi Cheng, Jun School of Computer Science and Information Security Guilin University of Electronic Technology GuangXi China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781728137261

Loop closure detection is a crucial module in simultaneous localization and mapping (SLAM), which reduces accumulative error in building environment map. Traditional appearance-based methods for loop closure detection are vulnerable to environmental variations as they mainly rely on hand-crafted features. The convolutional neural networks (ConvNets) can automatically learn feature representation from original image, and it is more robust to illumination changes. However, the ConvNets methods may fail when the viewpoint changes significantly due to it extract global features. In order to solve the problem mentioned above, in this paper, we design an unsupervised network which combines the advantage of the traditional and ConvNets methods, and propose a new module named spatial pyramid pooling based convolution autoencoder (SPP-CAE). We evaluate the performance of the proposed method on several open datasets using precision-recall metric. The results show that our method is feasible for detecting loops and is more robust than state-of-the-art methods. © 2019 IEEE.

关键词： Convolution

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A Machine Learning-Assisted Self-powered and Wearable Optical Sensor Glove for Gesture Recognition

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Journal of Lightwave technology 2025年

作者： Zou, Yang Lu, Chenxu Zhang, Chaomao Chen, Guozhen Kuang, Renfei Chen, Zhenshi Min, Rui Huang, Quandong Zhu, Shuyan Peng, Yungen Song, Jingcui Chen, Qingming Sun Yat-Sen University School of Microelectronics Science and Technology Guangdong Zhuhai519082 China Fujian Xiamen361005 China Guangdong 519000 China Sun Yat-Sen University School of Software Engineering Guangdong Zhuhai519082 China Huzhou College School of Electronic Information Zhejiang Huzhou313002 China Beijing Normal University Department of Psychology Faculty of Arts and Sciences Zhuhai519087 China Beijing Normal University Faculty of Psychology Beijing100875 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China Guangdong University of Technology Institute of Advanced Photonics Technology School of Information Engineering Guangzhou China

Gesture recognition is a vital component of human-computer interaction (HCI), which plays a key role in artificial intelligent (AI) technology. Self-powered, portable and wearable sensors are highly desirable for HCI applications. Optical wearable sensors have gained widespread attention in gesture recognition due to their high sensitivity and great biocompatibility. This paper reports a novel mechanoluminescent (ML) polymer optical fiber-based glove designed for self-powered and portable gesture recognition. The elastic ML optical fiber directly converts finger movements into fluorescence for strain sensing. Combined with the supplementary alumina nanoparticles, the fluorescence intensity of ZnS: Cu ML phosphors has been enhanced by 7 times to achieve a high-performance strain sensor. By integrating five ML fibers into a flexible printed circuit (FPC) with a wireless communication unit, we developed a smart glove capable of gesture detection. With the assistance of machine-learning training, the proposed glove can accurately identify six hand gestures and five grasping actions with recognition accuracies of 100% and 90%, respectively. This portable intelligent glove operates without external light sources and electrical drivers, making it highly suitable for potential applications in HCI and AI fields. © 1983-2012 IEEE.

关键词： Fiber optic sensors

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A symmetric uniform formula and sole index method for sieving (twin) primes

TechRxiv

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TechRxiv 2020年

作者： Ren, Wei School of Computer Science China University of Geosciences Wuhan430074 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University Guizhou China

All primes can be indexed by k, as primes must be in the form of 6k+1 or 6k-1. In this paper, we explore for the set of k such that either 6k+1 or 6k-1 is not a prime. Our method provides a uniform formula for k that can sieve primes and twin primes as well. The uniform presents symmetry in terms of k that works as single index in sieving. We also propose a new conjecture that is equivalent to Twin Prime Conjecture but possibly be easier to approach by merely exploring of sole index in term of k. The uniform formula for prime sieving are as follows: k ∈ Sl ⇒ 6k−1 6∈ , where Sl = [−I]6I+1 = [I]6I−1\min([I]6I−1), I ∈ . k ∈ Sr ⇒ 6k + 1 6∈ , where Sr = [−I]6I−1 ∪ ([I]6I+1\min([I]6I+1)), I ∈ . © 2020, CC BY.

关键词： Number theory

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DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge

arXiv

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arXiv 2020年

作者： Luo, Chunlong Yu, Tianqi Luo, Yufan Wang, Manqing Yu, Fuhai Li, Yinhao Tian, Chan Qiao, Jie Xiao, Li Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Peking University Third Hospital Beijing China School of Computer and Control Engineering University of Chinese Academy of China

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of prior knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criterion as a prior knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. 3390 metaphase images from clinical laboratory are collected and labelled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Chromosomes

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Multi-objective optimization on multistage half-band FIR filter design using simulated annealing algorithm 21

Multi-objective optimization on multistage half-band FIR fil...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Jin Hu, Wei Wang, Yonghao Zhu, Xiangyu College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System Wuhan China Digital Media Technology Lab Birmingham City University Birmingham United Kingdom

ISBN: (纸本)9781728120584

The design of a finite impulse response (FIR) digital filter is essentially a multi-parameter and multi-objective optimization problem, different optimization techniques can be utilized to minimize the latency in the live audio processing chain by minimization or maximization of the objective function. Using cascaded FIR filters as anti-aliasing and anti-image filters for high-resolution audio conversion are common. We can optimize the parameters of each stage to achieve the overall performance requirements. the method of modified Simulated Annealing (SA) Algorithm is proposed to resize the best distribution of passband ripple, transition band and stopband attenuation for each level of Multi-stage filter. It shows the proposed a new multi-stage filter design method improves the efficiency of the designed filter in terms of filter cost and group delay comparing with traditional design. © 2019 IEEE.

关键词： FIR filters

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Spectral-Spatial Weighted Sparse Nonnegative Tensor Factorization for Hyperspectral Unmixing

Spectral-Spatial Weighted Sparse Nonnegative Tensor Factoriz...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shaoquan Zhang Guorong Zhang Chengzhi Deng Jun Li Shengqian Wang Jun Wang Antonio Plaza Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China Hyperspectral Computing Laboratory Escuela Politécnica University of Extremadura Cáceres Spain

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Hyperspectral unmixing aims to decompose a hyperspectral image (HSI) into a collection of constituent materials, or end-members, and their corresponding abundance fractions. Recently, nonnegative tensor factorization (NTF)-based spectral unmixing methods have attracted significant attention owing to their outstanding performance when representing an HSI without any information loss. However, tensor factorization-based HSI methods do not fully exploit the spatial contextual information present in the scene. Besides, these approaches are sensitive to low signal-to-noise ratio (SNR) in HSIs. To address this limitation, we propose a new spectral-spatial weighted sparse nonnegative tensor factorization (SSWNTF) method to preserve the spatial details in the abundance maps via the spectral and spatial weighting factors. Our experiments with simulated data sets certified that the proposed method outperforms other advanced methods.

关键词： Hyperspectral imaging Tensors Signal to noise ratio Signal processing algorithms Matrix decomposition Sparse matrices Noise measurement

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Mobile data offloading in the vehicular networks: A truthful incentive mechanism 21

Mobile data offloading in the vehicular networks: A truthful...

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作者： Enfang, Zhong Li, Peng Zhang, Tao Huang, Weiyi Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Mobile data offloading is a cost-effective and promising approach to alleviate the burden of the cellular network. In this paper, we focus on designing a truthful incentive mechanism that the auction cloud platform motivates mobile users with deadline constraints and capacity constraints to offload data. First, we formulate the mobile data offloading problem as a reverse auction. Then, we reduce this social welfare maximization reverse auction for data offloading as an integer linear programming (ILP) problem. To solve this problem, we propose a data offloading auction greedy algorithm to choose the most cost-effective bids. We decompose the data offload auction greedy algorithm into the winner determination problem (WDP) and the threshold payment scheme. Moreover, we propose a delay winner winning algorithm based on the data offloading auction greedy algorithm. Extensive simulation experiments demonstrate that the data offloading auction mechanism dramatically reduces the burden of the cellular network while meeting the offloading tasks deadline constraints and mobile user capacity constraints. © 2019 IEEE.

关键词： Cost effectiveness

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Answer-supervised question reformulation for enhancing conversational machine comprehension 2

Answer-supervised question reformulation for enhancing conve...

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2nd Workshop on Machine Reading for Question Answering, MRQA@EMNLP 2019

作者： Li, Qian Su, Hui Niu, Cheng Wang, Daling Li, Zekang Feng, Shi Zhang, Yifei School of Computer Science and Engineering Northeastern University Shenyang China Pattern Recognition Center WeChat Ai Tencent Inc China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781950737819

In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset. © 2019 MRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering. All rights reserved.

关键词： Reinforcement learning

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Selective oxidation of benzyl alcohol to benzaldehyde using palladium nanoparticle-loaded poly(ionic liquid)s catalysts

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Molecular Catalysis 2025年 584卷

作者： Liu, Yaping Pan, Chao Qiu, Xiaomin Zhang, Hailin Feng, Haijun Tang, Yubao Shang, Zhijie Song, Hongbing College of Chemical Engineering Qingdao University of Science and Technology Zhengzhou Road No.53 Qingdao 266042 China China University of Petroleum (Beijing) School of Chemical Engineering and Environment State Key Laboratory of Heavy Oil Processing Beijing 102249 China School of Computer and Software Shenzhen Institute of Information Technology Guangdong Shenzhen 518172 China College of Chemistry and Molecular Engineering Qingdao University of Science and Technology Zhengzhou Road No.53 Qingdao 266042 China

The conversion of readily accessible chemicals into high-value-added products is a key process in industrial chemistry. This research showcases an efficient transformation of benzyl alcohol to benzaldehyde utilizing poly(ionic liquid)s (PILs) supported Pd-based catalysts. The catalyst's properties were thoroughly characterized using FT-IR, SEM, TEM, XPS, and TG, confirming the successful fabrication of stable Pd/PILs catalysts. The porous architecture of the PILs material enhances palladium nanoparticle adhesion, while its structure promotes intimate contact between the catalyst and substrate, facilitating the oxidation process. With just 0.1 g of 1 % Pd/PIL-2 catalyst, the reaction at 110 °C, 4 MPa O2 for 4 h achieve a 82.16 % conversion of benzyl alcohol with absolute selectivity to benzaldehyde. Theoretical calculations elucidated the interactions between Pd6 clusters, PIL, and benzyl alcohol. Furthermore, the Fukui function and optimized electrostatic potential diagrams identified reactive sites, providing insights into the potential reaction mechanism.

关键词： Benzaldehyde Benzyl alcohol Green oxidation Poly(ionic liquid)s Theoretical calculations

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Triple-view Learning for Predicting Antibiotic Resistance Genes

Triple-view Learning for Predicting Antibiotic Resistance Ge...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shujie Luo Haifang Wu Weizhong Zhao Xingpeng Jiang Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan PR China School of Computer Central China Normal University Wuhan PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin PR China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin PR China

ISBN: (纸本)9781665429825

With the increasing resistance of bacteria to antibiotics, the problem of antibiotic resistance has become a major challenge in healthcare. Therefore, the accurate identification of antibiotic resistance genes (ARGs) in bacteria is particularly urgent and important. Although culture-based methods can accurately identify ARGs, they are limited by domain knowledge and are time-consuming. With the availability of whole genome sequence data, most existing methods rely on sequence alignment to calculate sequence similarity to predict ARGs. However, for ARGs that has low sequence identity with known sequences, the existing methods do not identify them well. To address the challenges, we propose a multiple view learning based framework for ARGs. Experimental results demonstrate that the proposed approach performs better than the selected representative baselines on the real-world dataset.

关键词： Deep learning Microorganisms Antibiotics Semantics Genomics Medical services Predictive models

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