IEEE Transactions on Cognitive and Developmental Systems最新文献_第7页

Spatiotemporal Feature Enhancement Network for Blur Robust Underwater Object Detection 用于模糊鲁棒水下物体检测的时空特征增强网络

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-12 DOI: 10.1109/TCDS.2024.3386664

Hao Zhou;Lu Qi;Hai Huang;Xu Yang;Jing Yang

{"title":"Spatiotemporal Feature Enhancement Network for Blur Robust Underwater Object Detection","authors":"Hao Zhou;Lu Qi;Hai Huang;Xu Yang;Jing Yang","doi":"10.1109/TCDS.2024.3386664","DOIUrl":"10.1109/TCDS.2024.3386664","url":null,"abstract":"Underwater object detection is challenged by the presence of image blur induced by light absorption and scattering, resulting in substantial performance degradation. It is hypothesized that the attenuation of light is directly correlated with the camera-to-object distance, manifesting as variable degrees of image blur across different regions within underwater images. Specifically, regions in close proximity to the camera exhibit less pronounced blur compared to distant regions. Within the same object category, objects situated in clear regions share similar feature embeddings with their counterparts in blurred regions. This observation underscores the potential for leveraging objects in clear regions to aid in the detection of objects within blurred areas, a critical requirement for autonomous agents, such as autonomous underwater vehicles, engaged in continuous underwater object detection. Motivated by this insight, we introduce the spatiotemporal feature enhancement network (STFEN), a novel framework engineered to autonomously extract discriminative features from objects in clear regions. These features are then harnessed to enhance the representations of objects in blurred regions, operating across both spatial and temporal dimensions. Notably, the proposed STFEN seamlessly integrates into two-stage detectors, such as the faster region-based convolutional neural networks (Faster R-CNN) and feature pyramid networks (FPN). Extensive experimentation conducted on two benchmark underwater datasets, URPC 2018 and URPC 2019, conclusively demonstrates the efficacy of the STFEN framework. It delivers substantial enhancements in performance relative to baseline methods, yielding improvements in the mAP evaluation metric ranging from 3.7% to 5.0%.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1814-1828"},"PeriodicalIF":5.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Programmable Bionic Control Circuit Based on Central Pattern Generator 基于中央模式发生器的可编程仿生控制电路

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-12 DOI: 10.1109/tcds.2024.3388152

Qinghui Hong, Qing Li, Jia Li, Jingru Sun, Sichun Du

引用次数: 0

Unifying Obstacle Avoidance and Tracking Control of Redundant Manipulators Subject to Joint Constraints: A New Data-Driven Scheme 受联合约束的冗余机械手的统一避障和跟踪控制：数据驱动的新方案

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-11 DOI: 10.1109/TCDS.2024.3387575

Peng Yu;Ning Tan;Zhaohui Zhong;Cong Hu;Binbin Qiu;Changsheng Li

引用次数: 0

Deep Learning to Interpret Autism Spectrum Disorder Behind the Camera 深度学习解读镜头背后的自闭症谱系障碍

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-09 DOI: 10.1109/TCDS.2024.3386656

Shi Chen;Ming Jiang;Qi Zhao

{"title":"Deep Learning to Interpret Autism Spectrum Disorder Behind the Camera","authors":"Shi Chen;Ming Jiang;Qi Zhao","doi":"10.1109/TCDS.2024.3386656","DOIUrl":"10.1109/TCDS.2024.3386656","url":null,"abstract":"There is growing interest in understanding the visual behavioral patterns of individuals with autism spectrum disorder (ASD) based on their attentional preferences. Attention reveals the cognitive or perceptual variation in ASD and can serve as a biomarker to assist diagnosis and intervention. The development of machine learning methods for attention-based ASD screening shows promises, yet it has been limited by the need for high-precision eye trackers, the scope of stimuli, and black-box neural networks, making it impractical for real-life clinical scenarios. This study proposes an interpretable and generalizable framework for quantifying atypical attention in people with ASD. Our framework utilizes photos taken by participants with standard cameras to enable practical and flexible deployment in resource-constrained regions. With an emphasis on interpretability and trustworthiness, our method automates human-like diagnostic reasoning, associates photos with semantically plausible attention patterns, and provides clinical evidence to support ASD experts. We further evaluate models on both in-domain and out-of-domain data and demonstrate that our approach accurately classifies individuals with ASD and generalizes across different domains. The proposed method offers an innovative, reliable, and cost-effective tool to assist the diagnostic procedure, which can be an important effort toward transforming clinical research in ASD screening with artificial intelligence systems. Our code is publicly available at \u0000<uri>https://github.com/szzexpoi/proto_asd</uri>\u0000.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1803-1813"},"PeriodicalIF":5.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Semisupervised Object Segmentation for Long-Term Videos Using Adaptive Memory Network 利用自适应记忆网络为长期视频提供高效的半监督物体分割技术

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-08 DOI: 10.1109/TCDS.2024.3385849

Shan Zhong;Guoqiang Li;Wenhao Ying;Fuzhou Zhao;Gengsheng Xie;Shengrong Gong

{"title":"Efficient Semisupervised Object Segmentation for Long-Term Videos Using Adaptive Memory Network","authors":"Shan Zhong;Guoqiang Li;Wenhao Ying;Fuzhou Zhao;Gengsheng Xie;Shengrong Gong","doi":"10.1109/TCDS.2024.3385849","DOIUrl":"10.1109/TCDS.2024.3385849","url":null,"abstract":"Video object segmentation (VOS) uses the first annotated video mask to achieve consistent and precise segmentation in subsequent frames. Recently, memory-based methods have received significant attention owing to their substantial performance enhancements. However, these approaches rely on a fixed global memory strategy, which poses a challenge to segmentation accuracy and speed in the context of longer videos. To alleviate this limitation, we propose a novel semisupervised VOS model, founded on the principles of the adaptive memory network. Our proposed model adaptively extracts object features by focusing on the object area while effectively filtering out extraneous background noise. An identification mechanism is also thoughtfully applied to discern each object in multiobject scenarios. To further reduce storage consumption without compromising the saliency of object information, the outdated features residing in the memory pool are compressed into salient features through the employment of a self-attention mechanism. Furthermore, we introduce a local matching module, specifically devised to refine object features by fusing the contextual information from historical frames. We demonstrate the efficiency of our approach through experiments, substantially augmenting both the speed and precision of segmentation for long-term videos, while maintaining comparable performance for short videos.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1789-1802"},"PeriodicalIF":5.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GENet: A Generic Neural Network for Detecting Various Neurological Disorders From EEG GENet：从脑电图检测各种神经系统疾病的通用神经网络

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-08 DOI: 10.1109/TCDS.2024.3386364

Md. Nurul Ahad Tawhid;Siuly Siuly;Kate Wang;Hua Wang

{"title":"GENet: A Generic Neural Network for Detecting Various Neurological Disorders From EEG","authors":"Md. Nurul Ahad Tawhid;Siuly Siuly;Kate Wang;Hua Wang","doi":"10.1109/TCDS.2024.3386364","DOIUrl":"10.1109/TCDS.2024.3386364","url":null,"abstract":"The global health burden of neurological disorders (NDs) is vast, and they are recognized as major causes of mortality and disability worldwide. Most existing NDs detection methods are disease-specific, which limits an algorithm's cross-disease applicability. A single diagnostic platform can save time and money over multiple diagnostic systems. There is currently no unified standard platform for diagnosing different types of NDs utilizing electroencephalogram (EEG) signal data. To address this issue, this study aims to develop a generic EEG neural Network (GENet) framework based on a convolutional neural network that can identify various NDs from EEG. The proposed framework consists of several parts: 1) preparing data using channel reduction, resampling, and segmentation for the GENet model; 2) designing and training the GENet model to carry out important features for the classification task; and 3) assessing the proposed model's performance using different signal segment lengths and several training batch sizes and also cross-validating using seven different EEG datasets of six distinct NDs namely schizophrenia, autism spectrum disorder, epilepsy, Parkinson's disease, mild cognitive impairment, and attention-deficit/hyperactivity disorder. In addition, this study also investigates whether the proposed GENet model can identify multiple NDs from EEG. The proposed model achieved much better performance for both binary and multiclass classification compared to state-of-the-art methods. In addition, the proposed model is validated using several ablation studies and layerwise feature visualization, which provide consistency and efficiency to the proposed model. The proposed GENet model will help technologists create standard software for detecting any of these NDs from EEG.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1829-1842"},"PeriodicalIF":5.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Computational Intelligence Society 电气和电子工程师学会计算智能学会

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-04 DOI: 10.1109/TCDS.2024.3373153

引用次数: 0

IEEE Transactions on Cognitive and Developmental Systems Publication Information 电气和电子工程师学会认知与发展系统论文集》出版信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-04 DOI: 10.1109/TCDS.2024.3373151

引用次数: 0

Guest Editorial Special Issue on Movement Sciences in Cognitive Systems 认知系统中的运动科学》特刊客座编辑

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-04 DOI: 10.1109/TCDS.2024.3372274

Junpei Zhong;Ran Dong;Soichiro Ikuno;Yanan Li;Chenguang Yang

引用次数: 0

IEEE Transactions on Cognitive and Developmental Systems Information for Authors 电气和电子工程师学会《认知与发展系统》期刊为作者提供的信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-04 DOI: 10.1109/TCDS.2024.3373155

引用次数: 0