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A multi-feature fusion approach for electroencephalogram personality analysis

A multi-feature fusion approach for electroencephalogram per...

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2024 International conference on computer Graphics, Artificial Intelligence, and Data processing. ICCAID 2024

作者： Liao, Yijie Li, Zhengxiu Jie, Lilin Cheng, Ruipeng Li, Ming Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang330063 China School of Information Engineering Nanchang Hangkong University Nanchang330063 China

ISBN: (纸本)9781510689275

Electroencephalogram(EEG) signals have the potential to objectively analyze personality, but most of the existing personality analysis studies use a single EEG feature. In order to improve the accuracy of personality analysis, this paper proposes an EEG multi-feature fusion method for personality analysis. Firstly, a personality test paradigm which covers three emotional stimuli was designed, and the EEG data of 22 subjects were acquired;the preprocessed EEG data were extracted to contain multiple EEG features in the time domain, frequency domain, time-frequency domain, and nonlinear features. Then a multi-layer perceptual machine and sparse linear discriminant analysis were employed to integrate and reduce the extracted multi-electroencephalographic features, and the classification task was carried out using the deep learning network CNN-Transformer. Compared with other methods, the method proposed in this paper has an improvement in the analysis accuracy of the five personality dimensions of extraversion, agreeableness, conscientiousness, neuroticism, and openness by 4.6%-23.91%, 8.98%-34.5%, 7.89%-34.5%, 5.5%-30.5%, and 2.88%-22.88% respectively. © 2025 SPIE.

关键词： Bode diagrams

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Gas/Odor Qualitative and Quantitative Estimation Methodology for a Bionic Electronic Nose

Gas/Odor Qualitative and Quantitative Estimation Methodology...

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2023 ieee Biomedical Circuits and Systems conference, BioCAS 2023

作者： Zhang, Wenwen Zhang, Zhengyuan Zhongzhiguang, Lu Lin, Zhiping Zheng, Yuanjin Nanyang Technological University School of Electrical and Electronics Engineering Singapore Singapore University of Shanghai for Science and Technology School of Science School of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798350300260

The electronic nose (E-nose) efficiently detects the composition and concentration of gases within a remarkably short span, enabling the identification of volatile metabolites emitted by the human body. However, Traditional methods for both qualitative and quantitative gas estimation face challenges in efficiently employing real-time signals from electronic noses, thereby resulting in imprecise online gas qualitative and quantitative estimation. To address this limitation, our research introduces a cascaded approach that integrates two models: the Muti-scale Wavelet Coefficient image-Capsule Network (MWCI-CapsNet) for gas identification and the weighted real-time signal-cosformer (weighted-cosformer) for concentration estimation. This approach directly utilizes the real-time signal data from the E-nose as input, eliminating the need for signal pre-processing. By conducting experiments aimed at identifying CO, H2, and mixed gas compositions of CO and H2 using our custom-developed bionic electronic nose, our proposed MWCI-CapsNet model achieves a remarkable accuracy approaching 100% in gas recognition while employing a reduced number of annotated data samples. Furthermore, the weighted-cosformer model demonstrates superior performance when contrasted with the conventional gas quantitative estimation model. © 2023 ieee.

关键词： Capsule Network electronic nose gas identification wavelet coefficient

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A Drug Box recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation 29

A Drug Box Recognition System Based on Deep Learning and Clo...

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29th ieee International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhu, Honglei Dong, Anming Yu, Jiguo School of Computer Science and Technology Jinan China Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor's prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it's hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement. © 2023 ieee.

关键词： Cloud-Edge Collaboration image recognition Pharmacy Automation Text recognition

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Brain graph synthesis for multi-site autism spectrum disorder identification

Brain graph synthesis for multi-site autism spectrum disorde...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Li, Wenqi Huang, Huifang School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510666351

Recently, machine learning attracts more attention on autism spectrum disorder (ASD) identification based on resting-state functional magnetic resonance imaging (rs-fMRI). Most studies on ASD identification use rs-fMRI data from multiple imaging sites to increase sample size, but they suffer from the data heterogeneity among sites. Besides, most ASD identification studies use features simply extracted from brain connections, ignoring the topological structure of brain. To address these issues, we propose a brain graph synthesis model based on generative adversarial network (GAN), which transforms data from the source domain to the target domain, solving the data heterogeneity among sites. Specifically, the generator and discriminator are designed for graph structured data and propose topological losses to construct the cycle consistency loss for maintaining the original topological structure of the reconstructed brain graph. We carry out experiments on six tasks using the open database Autism Brain Imaging Data Exchange (ABIDE) for ASD identification. Experimental findings demonstrate that our model can solve the problem of data heterogeneity effectively and achieve satisfying performance on multi-site ASD identification. © 2023 SPIE.

关键词： Generative adversarial networks

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CoCV: Heterogeneous Processors Collaboration Mechanism for End-to-End Execution of Intelligent computer Vision Tasks on Mobile Devices 29

CoCV: Heterogeneous Processors Collaboration Mechanism for E...

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29th ieee International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wan, Ye Liu, Mengyang Li, Guangtong Dong, Fang Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350330717

Object detection, image classification, and various other computer vision tasks have become prevalent on mobile devices. These computer vision tasks are typically executed with three stages: pre-processing. inference, and post-processing. Mobile SoC, serving as the computing unit on mobile devices, typically consists of heterogeneous processors like CPU, GPU, and NPU. However, during the execution of a computer vision task, current available frameworks only achieve the parallelism of CPU and GPU in the inference stage. While during pre- and post-processing. only CPU is used, leaving GPU and NPU on the SoC to be idle. For mobile applications that require low latency, the overhead of pre-processing.and post-processing.stages often account for more than 50% of the total latency, which becoming a performance bottleneck of the entire task. To reduce latency, it is imperative to fully utilize the idle heterogeneous processors (GPU, NPU) on the SoC and achieve heterogeneous processors parallelism in all three stages during execution. In this paper, we propose CoCV, a heterogeneous processor parallel computing system for computer vision tasks on mobile devices. In CoCV, we are the first to build an image processing.operator library for heterogeneous parallel computing on mobile devices. Besides, we design a task allocation scheduling algorithm to guide the partitioning of processing.tasks during execution, which ensuring a relatively balanced workload between different processors. A cross-stage operator chaining technique is also proposed to reduce the data sharing overhead among different processors during execution. We build a prototype system and evaluate it with different computer vision tasks. The results show up to 33% latency reduction for end-to-end tasks and 2.32× speedup compared with the current best solution. © 2023 ieee.

关键词： CPU&GPU Co-execution Heterogeneous Computing Mobile devices

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Electroencephalogram-based dynamic continuous emotion recognition method

Electroencephalogram-based dynamic continuous emotion recogn...

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2024 International conference on computer Graphics, Artificial Intelligence, and Data processing. ICCAID 2024

作者： Liu, Yong Jie, Lilin Li, Zhengxiu Zou, Yangmeng Li, Junhua Key Laboratory of Jiangxi Provincial for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang330036 China School of Instrument Science and Optoelectronic Engineering Nanchang Hangkong University Nanchang330036 China

ISBN: (纸本)9781510689275

Human emotions are dynamic and influenced by external circumstances, social interactions, and an individual's internal state. However, existing electroencephalogram (EEG) emotion recognition research often focuses on static scenarios and does not fully account for the continuity of dynamic emotions. To overcome these limitations, this paper proposes an dynamic continuous emotion recognition approach based on EEG signals. Firstly, a novel experimental paradigm was designed to cover six emotion transition scenarios: calm to happy, happy to calm, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG signals were collected from 24 subjects using a 64-channel EEG device. These signals were assigned dynamic continuous emotional labels, which were normalized to ensure consistency. Secondly, the brain was divided into four zones: frontal, parietal, occipital and temporal. Five regression algorithms with good performance were selected for integration. A dynamic emotion recognition model was then constructed, which included a regional feature extraction module and an integrated regression module, aiming at identifying EEG emotions in dynamic situations. Finally, the differential entropy feature was tested, and the results showed the specificity of different brain zones in recognizing the six dynamic emotions. Moreover, the proposed model achieved lower mean square errors in valence and arousal. Compared to six machine learning models and deep learning models, namely SVRrbf, KNN, DT, RF, CNN, and GRU, the proposed model demonstrates higher recognition accuracy. © 2025 SPIE.

关键词： Feature Selection

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Pseudo-label based and transformation-consistent self-ensembling model for underwater life images

Pseudo-label based and transformation-consistent self-ensemb...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Huang, Shan Luo, Xiaonan Lu, Jiahao Huang, Chao Xu, Chengpei Yuan, Tianran School of Computer Science and Information Security Guilin University of Electronics Technology No. 1 Jinji Road Guilin541004 China

ISBN: (纸本)9781510666351

With the continuous development of underwater vision technology, high-precision object segmentation has received more attention from scholars. Due to the complexity of the underwater environment, the segmentation of underwater objects has become a new challenge. In order to alleviate such problems, this paper proposes a semi-supervised segmentation model based on pseudo-labels and transformation-consistent(PSTM). Due to the complexity of underwater images, PSTM preprocesses underwater biological images through an image enhancement module. A semi-supervised segmentation network with pseudo-labels and transformation-consistent is then used to obtain a more robust segmentation result. The method in this paper achieves better performance on the public underwater biological dataset DUT-USEG. © 2023 SPIE.

关键词： Deep neural networks

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Semi-auto labeling device for 6Dof object pose estimation

Semi-auto labeling device for 6Dof object pose estimation

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2022 International conference on image, Signal processing. and pattern recognition, ISPP 2022

作者： Liao, Meizhen Wei, Wei Zhang, Xiaojie Long, Yuzhong Li, Hanxi School of Computer and Information Engineering Jiangxi Normal University 330022 China SpritAR Shanghai200000 China

ISBN: (纸本)9781510654846

In this work, we propose to generate the supervised training dataset for 6-DoF object pose estimation algorithm, with trivial human labor. A semi-auto labelling board is designed so that its pose can be estimated accurately with a learned deep model. For a training sequence with more than 1000 frames, one only needs to manually mark the key points of the object for 2-5 frames and the ground-truth pose of the object can be calculated automatically for the whole sequence. In the experiment, we prove that the state-of-the-art pose estimation method can be trained well with only 20-50 human-labelled images and the yielded model performs better than the model learned based on the manually labelled dataset with more than 800 images. © 2022 SPIE.

关键词： computer vision

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Detecting the Foreground Dynamic Scenes using Gaussian Mixture Model Analysis Compared with Novel Principal Component Analysis 2

Detecting the Foreground Dynamic Scenes using Gaussian Mixtu...

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2nd International conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Baswaraju, Swathi New Horizon College of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350325799

Many computer vision applications rely on object recognition in video streams, and the most common technique for doing so is called foreground subtraction. updated backdrop model with a new frame. However, precise object identification is challenging in static and dynamic foregrounds. This study proposes a novel approach. Recently, Novel Principal Component Analysis, also known as (NPCA), has been used extensively across the industry for dimension reduction in image processing. presentation, and pattern identification to this issue that is based on the Gaussian Mixture Model (GMM) technique. A new updating method has been implemented to achieve a more stable model for the dynamic zones. This study provides a comprehensive overview and evaluation of other subtraction methods. The experimental results demonstrate the superiority of the proposed method over numerous top-tier object detection methods. © 2023 ieee.

关键词： Principal component analysis

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A New Model for Prototype-based Continual Learning in Hyperspherical Space

A New Model for Prototype-based Continual Learning in Hypers...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Ren, Yixin Xia, Yewei Ke, Li Huang, Longtao Xue, Hui Zhou, Shuigeng School of Computer Science Fudan University Shanghai200438 China Alibaba Group Hangzhou311121 China

ISBN: (纸本)9798350368741

The continuous emergence of new objects in the visual world poses a serious challenge to deep object recognition methods, which sparks the increasing study on continual or incremental learning. However, learning new tasks faces the tough catastrophic forgetting problem, i.e., dramatic performance degradation on old tasks. A good continual learning model should be robustly adapted to the upcoming tasks while effectively handling catastrophic forgetting. In this paper, we focus on the class-incremental learning (CIL) task, and propose a novel prototype-based continual learning model C-HPN that projects the visual features into a hypersphere geometric space, where continual learning is conducted. C-HPN features two-fold contributions. On the one hand, instead of using the popular cross-entropy loss, we develop an instance-prototype compact loss to obtain well-clustered hyperspherical embeddings and a prototype-prototype separability loss to boost the model's generalization by introducing large angle distance inductive bias between prototypes in the hyperspherical space. On the other hand, prototype construction and adaptation strategies are designed for effectively adapting new classes, and an instance-prototype relationship preservation distillation mechanism is introduced to overcome catastrophic forgetting. Extensive experiments on several image datasets validate the effectiveness of the proposed method. © 2025 ieee.

关键词： class-incremental learning continual learning hypersphere geometry prototype networks

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