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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 761 条记录，以下是581-590 订阅

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Event Detection and Summarisation of Live Tweets Using SCAN Algorithms 3rd

Event Detection and Summarisation of Live Tweets Using SCAN ...

引用

3rd international conference on Data, Engineering, and Applications, IDEA 2021

作者： Jain, Tavishi Singh, Bhavya Dewang, Rupesh Kumar Department of Computer Science & Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789811946868

Twitter is one of the profitable sources of event-related data, to be specific, breaking news and local and global event reports. Due to its capacity to transmit data progressively, Twitter is exploited to detect events and generate a summary. Such detected events and their summaries can help the users and organisations to acquire actionable knowledge and respond to the situations accordingly. The present paper focuses on extracting events by analysing the text stream in Twitter in real-time. Our approach includes 3 stages: Forming key graph, forming clusters of keywords using SCAN (Structural Clustering Algorithm for Networks) and then summarising events. In our proposed approach, all the above processing is done on tweets collected in batches using spark streaming to analyse their results in real-time, enabling users and organisations to respond immediately. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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A Perception Method Based on Point Cloud processing in Autonomous Driving 1

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Huang, Qiuqin Huang, Jiangshuai Sheng, Xingyue Yue, Xiaowen Chongqing University 174 Shazheng Street Shapingba District Chongqing China Chongqing Zhi Xiang Paving Technology Engineering CO. LTD. Chongqing China

ISBN: (数字)9789811961359

ISBN: (纸本)9789811961342

The abstract should briefly summarize the contents In the field of autonomous driving, autonomous cars need to perceive and understand their surroundings autonomously. At present, the most commonly used sensors in the field of driverless cars are RGB-D camera and LIDAR. Therefore, how to process the environmental information collected by these sensors, and extract the features we are interested in, and then use them to guide the driving of unmanned vehicles, has become an essential research point in the field of autonomous driving. Among them, compared with 2D image information, 3D point cloud can provide 3D orientation information of objects that 2D image does not have. Based on this, how to accurately process and perceive 3D point cloud and separate objects such as obstacles, cars and roads is crucial to the safety of autonomous driving. This paper adopts a method of preprocessing point cloud data and enhancing point cloud with images. KITTI dataset is the most classic and representative dataset in the field of autonomous driving. In the experiment, KITTI dataset is used to verify that this method can get good perception effect. In addition, we use the bird’s-eye view benchmark on KITTI to evaluate the performance of the original network. It is found that the improvement effect of this experiment on the original network is also very obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image fusion

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Design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm 3rd

Design of Portrait System for Road Safety Based on a Dynamic...

引用

3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Li, Chenxing Cui, Yongchuan Hu, Chengyu School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9789811961342

Traffic accidents are a safety issue that has received widespread attention. Practical mining of the causes of traffic accidents can lay the foundation for early warning of traffic accidents. The emerging user profiling technology can solve this problem very effectively, and the clustering algorithm is the key to realizing user profiling. In this paper, we have designed a portrait system for road safety based on dynamic density clustering algorithms and have developed an architecture and functional requirements for the system. To do this, we use an actual data sample representing road accidents in the United Kingdom (UK) between 2005 and 2014 for data mining and use the dynamic density clustering algorithm to cluster the dataset, discover the data flow trends of safety accidents from 2005 to 2014, discuss the road safety accident-prone scenarios, and combine the system business with realistic road safety management. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images 25th

Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic R...

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25th international conference on Medical Image Computing and computer-Assisted Intervention , MICCAI 2022

作者： Hou, Junlin Xiao, Fan Xu, Jilan Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031336577

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the first task of segmentation of DR lesions, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the second task of image quality assessment, we create an ensemble of Inception-V3, SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our models. In the third task of DR grading, we build a Vision Transformer and find that the model pre-trained on color fundus images serves as a useful substrate for OCTA images. Extensive ablation studies demonstrate the effectiveness of each designed component in our solutions. The proposed methods rank 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. Our code is publicly available at https://***/FDU-VTS/DRAC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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EEG Based Stress Classification in Response to Stress Stimulus 3rd

EEG Based Stress Classification in Response to Stress Stimul...

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

作者： Phutela, Nishtha Relan, Devanjali Gabrani, Goldie Kumaraguru, Ponnurangam Computer Science and Engineering Department BML Munjal University Haryana Gurugram India College of Engineering Vivekananda Institute of Professional Studies Pitampura New Delhi India Computer Science Department IIIT Hyderabad Hyderabad Gachibowli India

ISBN: (纸本)9783030957100

Stress, either physical or mental, is experienced by almost every person at some point in his lifetime. Stress is one of the leading causes of various diseases and burdens society globally. Stress badly affects an individual's well-being. Thus, stress-related study is an emerging field, and in the past decade, a lot of attention has been given to the detection and classification of stress. The estimation of stress in the individual helps in stress management before it invades the human mind and body. In this paper, we proposed a system for the detection and classification of stress. We compared the various machine learning algorithms for stress classification using EEG signal recordings. Interaxon Muse device having four dry electrodes has been used for data collection. We have collected the EEG data from 20 subjects. The stress was induced in these volunteers by showing stressful videos to them, and the EEG signal was then acquired. The frequency-domain features such as absolute band powers were extracted from EEG signals. The data were then classified into stress and non-stressed using different machine learning methods - Random Forest, Support Vector Machine, Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Gradient Boosting. We performed 10-fold cross-validation, and the average classification accuracy of 95.65% was obtained using the gradient boosting method. © 2022, Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Named Entity Disambiguation Based on Bidirectional Semantic Path 3rd

Named Entity Disambiguation Based on Bidirectional Semantic...

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3rd international conference on Artificial Intelligence, 2022

作者： Li, Zimao Zhang, Yue Yin, Fan Nie, Mengyan College of Computer Science South-Central University for Nationalities Wuhan430074 China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074 China

ISBN: (纸本)9789811694226

In the existing entity disambiguation algorithms, the full connection method is adopted to measure the association between candidate entities, ignoring the ambiguity of entity mention, and the shortest path feature used for feature extraction does not consider the influence of different path starting and ending points. Therefore, this paper proposes a progressive named entity disambiguation (NED) algorithm PNED, which uses the bidirectional semantic association feature and topic relevance feature extracted from the disambiguated entity to calculate the association relation between entities. The algorithm fully considers the difficulty of different entity mention disambiguation, and gradually completes the NED task from easy to difficult, which improves the implementation efficiency of NED. Experimental results show the effectiveness of the algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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High-speed Mobile Real-time Topology Optimization of Amorphous Flattened Air-to-ground Wireless Self-organizing Network Nodes Based on Dynamic Planning 3

High-speed Mobile Real-time Topology Optimization of Amorpho...

引用

3rd international conference on Electronics and Communication;Network and computer Technology (ECNCT)

作者： Wang, Zhifang Yu, Jianguo Shangjing Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

To address the topological instability in the amorphous flat air-ground wireless self-organizing network caused by the high-speed movement of the network nodes, this paper adopts the 802.15.4 protocol as the basis of research, and uses dynamic planning in optimal control theory to backpropagate and optimize the directional steering angle of the mobile trajectories of the air and ground self-organizing nodes, so as to maximize the elimination of the wireless signal coverage blind area and enable as many self-organizing nodes as possible to form a stable, effective and reliable self-organizing network topology. This can maximize the elimination of wireless signal coverage blind areas and enable as many selfassembling nodes as possible to form a stable, effective and reliable self-assembling network topology. The simulation results show that by using the dynamic planning strategy, the optimized steering angle of the moving trajectory direction corresponding to the self-grouping nodes at different moving speeds can be obtained, and the sum of squares of the steering angles of the instantaneous moving trajectory direction of the optimized nodes can be obtained. The experimental results show that three fixed ground self-grouping nodes, one vehicle self-grouping node and one air self-grouping node are used to test the topology of high-speed mobile self-grouping network, and the five nodes can finally form a stable and effective self-grouping network topology.

关键词： Amorphous flat air-to-ground self-organizing network High-speed mobile Dynamic planning Moving track direction steering angle Wireless signal patching

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Low-Power MISO Biquadratic Filter and Diode-Less Full Wave Rectifier Using CM-BD-MO-OTA for Bio-signals 3rd

Low-Power MISO Biquadratic Filter and Diode-Less Full Wave R...

引用

3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Sharan, Tripurari Richa, Akho John ECE Department North Eastern Regional Institute of Science and Technology Deemed to be University Arunachal Pradesh Nirjuli791109 India

ISBN: (纸本)9789811627606

An ultra-low-power, weak inversion region biased, bulk-driven (BD), flipped voltage follower (FVF) and self-cascode (SC) load-based multiple outputs (MO) OTA addressed as (SC-CM-BD-MO-OTA) is presented in this paper. It has also utilized partial positive feedback (PPF) at load ends. This OTA when powered by ±0.3 V dual power supply provides enhanced fully differential gain, unity gain bandwidth (GBW) and phase margin of 78 dB, 13.5 kHz and 63°, respectively. This OTA has been employed as low-frequency low-power diode-less non-inverting and inverting full wave rectifiers, multiple input single output (MISO)-type biquadratic filter usable for wave shaping and filtering of biomedical signals. The full wave rectifiers ensured good rectification performance for sine wave input of frequency range (1–500 Hz) and amplitude range (100 µV to 30 mV) while consuming a total power of 104 nW only. The designed MISO filter is usable as low-frequency range band limiting and output smoothing filter in bio-signal processing chain. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rectifying circuits

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A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis 1

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Jiang, Huiming Li, Song Shao, Yunfei Xu, An Yuan, Jing Zhao, Qian University of Shanghai for Science and Technology 516 Jungong Rd Shanghai200093 China Shanghai Aircraft Manufacturing Co. Ltd 919 Shangfei Road Shanghai201324 China

ISBN: (数字)9789811961359

ISBN: (纸本)9789811961342

Aiming at the limitation that the single channel source of rolling bearing contains incomplete fault information and the strong correlation and coupling relationship between different channels, a multi-channel fusion diagnosis method for rolling bearing based on multi-domain tensor feature and support tensor machine is proposed. In order to fuse the bearing fault information contained in multi-channel vibration signals, firstly, multi-domain features such as time domain features, wavelet domain features, entropy domain features and trigonometric function features of each channel signal are extracted respectively, and the third-order tensor with channel, feature and time as dimensions is constructed. Then, the CP rank is determined by the kernel consistent diagnosis method, and the high-order fault state information contained in the multi-domain tensor is extracted by the low rank tensor approximation method based on CP decomposition to obtain the multi-domain tensor characteristics of multi-channel rolling bearing. Finally, the multi-domain tensor feature is input into the support tensor machine for bearing fault diagnosis. The example of bearing fault diagnosis shows that the multi-channel fusion diagnosis method based on multi-domain tensor feature and support tensor machine proposed in this paper can make full use of the high-order structural features of multi-channel information source, fuse multi-channel information to more comprehensively characterize the fault state of bearing, and obtain higher diagnosis accuracy than single channel information source. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Roller bearings

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Dual-Band Pentagonal-Shape Hybrid Rectangular Dielectric Resonator Antenna for C-Band Application 3rd

Dual-Band Pentagonal-Shape Hybrid Rectangular Dielectric Res...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Ranjan, Pinku Patil, Mihir Sharma, Anand Atal Bihari Vajpayee-Indian Institute of Information Technology and Management Gwalior India Electro Science Laboratory Ohio State University ColumbusOH United States Department of Electronics and Communication Engineering MotiLal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789811627606

Design and analysis of pentagonal antenna with rectangular dielectric resonator antennas for X-Band application has been presented in this paper. It is a dual-band antenna resonating at 3.72–5.8 GHz and 6.38–8 GHz, with total impedance bandwidth of 3.7 GHz, which is resonating at 4.42 GHz and 7.63 GHz, respectively. The dimension of the antenna is 30 × 30 × 1.6 mm3, and dimension of rectangular DR is 13 × 13 × 6 mm3 placed on the pentagonal patch. The DR is made up of Al2O3 (alumina ceramic). Since this antenna is designed for wideband applications, the DR is placed to increase the impedance bandwidth of the proposed antenna. The radiation efficiency greater than 95% and the gain of 3.13 dBi are achieved by the proposed antenna. It is a compact and low-profile antenna. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Radiation efficiency

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