Cognitive Robotics最新文献_第2页

Integrated model for segmentation of glomeruli in kidney images 肾脏图像中肾小球分割的集成模型

Cognitive Robotics Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.11.007

Gurjinder Kaur, Meenu Garg, Sheifali Gupta

{"title":"Integrated model for segmentation of glomeruli in kidney images","authors":"Gurjinder Kaur, Meenu Garg, Sheifali Gupta","doi":"10.1016/j.cogr.2024.11.007","DOIUrl":"10.1016/j.cogr.2024.11.007","url":null,"abstract":"<div><div>Kidney diseases, especially those that affect the glomeruli, have become more common worldwide in recent years. Accurate and early detection of glomeruli is critical for accurately diagnosing kidney problems and determining the most effective treatment options. Our study proposed an advanced model, FResMRCNN, an enhanced version of Mask R-CNN, for automatically detecting and segmenting the glomeruli in PAS-stained human kidney images. The model integrates the power of FPN with a ResNet101 backbone, which was selected after assessing seven different backbone architectures. The integration of FPN and ResNet101 into the FResMRCNN model improves glomeruli detection, segmentation accuracy and stability by representing multi-scale features. We trained and tested our model using the HuBMAP Kidney dataset, which contains high-resolution PAS-stained microscopy images. During the study, the effectiveness of our proposed model is examined by generating bounding boxes and predicted masks of glomeruli. The performance of the FResMRCNN model is evaluated using three performance metrics, including the Dice coefficient, Jaccard index, and binary cross-entropy loss, which show promising results in accurately segmenting glomeruli.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 1-13"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks 基于混合机器学习的无人机辅助无线网络三维无人机节点定位

Cognitive Robotics Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.01.002

Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

{"title":"Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks","authors":"Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee","doi":"10.1016/j.cogr.2025.01.002","DOIUrl":"10.1016/j.cogr.2025.01.002","url":null,"abstract":"<div><div>This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. The proposed model efficiently managed interference, adapted to UAV mobility, and ensured optimal throughput by dynamically optimizing UAV trajectories. The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. Simulation results demonstrated that the proposed model outperformed traditional machine learning models, significantly improving energy efficiency, latency, and throughput. The hybrid model achieved an optimized energy efficiency of 90 Tbps/J, reduced latency to 0.0105 ms, and delivered a network throughput of approximately 96 Tbps. The model adapts to varying link densities, maintaining stable performance even in high-density scenarios. These findings underscore the framework's potential to address key challenges in UAV-assisted 6G networks, paving the way for scalable and efficient communication in next-generation wireless systems.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 61-76"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LiPE: Lightweight human pose estimator for mobile applications towards automated pose analysis LiPE：用于移动应用程序的轻量级人体姿势估计器，用于自动姿势分析

Cognitive Robotics Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.11.005

Chengxiu Li , Ni Duan

引用次数: 0

Robotic terrain classification based on convolutional and long short-term memory neural networks 基于卷积和长短期记忆神经网络的机器人地形分类

Cognitive Robotics Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.04.002

YiGe Hu

引用次数: 0

Mobile robot path planning using deep deterministic policy gradient with differential gaming (DDPG-DG) exploration 利用深度确定性策略梯度与微分博弈（DDPG-DG）探索移动机器人路径规划

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.08.002

Shripad V. Deshpande , Harikrishnan R , Babul Salam KSM Kader Ibrahim , Mahesh Datta Sai Ponnuru

{"title":"Mobile robot path planning using deep deterministic policy gradient with differential gaming (DDPG-DG) exploration","authors":"Shripad V. Deshpande , Harikrishnan R , Babul Salam KSM Kader Ibrahim , Mahesh Datta Sai Ponnuru","doi":"10.1016/j.cogr.2024.08.002","DOIUrl":"10.1016/j.cogr.2024.08.002","url":null,"abstract":"<div><p>Mobile robot path planning involves decision-making in uncertain, dynamic conditions, where Reinforcement Learning (RL) algorithms excel in generating safe and optimal paths. The Deep Deterministic Policy Gradient (DDPG) is an RL technique focused on mobile robot navigation. RL algorithms must balance exploitation and exploration to enable effective learning. The balance between these actions directly impacts learning efficiency.</p><p>This research proposes a method combining the DDPG strategy for exploitation with the Differential Gaming (DG) strategy for exploration. The DG algorithm ensures the mobile robot always reaches its target without collisions, thereby adding positive learning episodes to the memory buffer. An epsilon-greedy strategy determines whether to explore or exploit. When exploration is chosen, the DG algorithm is employed. The combination of DG strategy with DDPG facilitates faster learning by increasing the number of successful episodes and reducing the number of failure episodes in the experience buffer. The DDPG algorithm supports continuous state and action spaces, resulting in smoother, non-jerky movements and improved control over the turns when navigating obstacles. Reward shaping considers finer details, ensuring even small advantages in each iteration contribute to learning.</p><p>Through diverse test scenarios, it is demonstrated that DG exploration, compared to random exploration, results in an average increase of 389% in successful target reaches and a 39% decrease in collisions. Additionally, DG exploration shows a 69% improvement in the number of episodes where convergence is achieved within a maximum of 2000 steps.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 156-173"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000119/pdfft?md5=8c083de5d6ac1af9d3cedcb0733a30fa&pid=1-s2.0-S2667241324000119-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emerging trends in human upper extremity rehabilitation robot 人体上肢康复机器人的新趋势

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.09.001

Sk. Khairul Hasan, Subodh B. Bhujel, Gabrielle Sara Niemiec

{"title":"Emerging trends in human upper extremity rehabilitation robot","authors":"Sk. Khairul Hasan, Subodh B. Bhujel, Gabrielle Sara Niemiec","doi":"10.1016/j.cogr.2024.09.001","DOIUrl":"10.1016/j.cogr.2024.09.001","url":null,"abstract":"<div><p>Stroke is a leading cause of neurological disorders that result in physical disability, particularly among the elderly. Neurorehabilitation plays a crucial role in helping stroke patients recover from physical impairments and regain mobility. Physical therapy is one of the most effective forms of neurorehabilitation, but the growing number of patients requires a large workforce of trained therapists, which is currently insufficient. Robotic rehabilitation offers a promising alternative, capable of supplementing or even replacing human-assisted physical therapy through the use of rehabilitation robots. To design effective robotic devices for rehabilitation, a solid foundation of knowledge is essential. This article provides a comprehensive overview of the key elements needed to develop human upper extremity rehabilitation robots. It covers critical aspects such as upper extremity anatomy, joint range of motion, anthropometric parameters, disability assessment techniques, and robot-assisted training methods. Additionally, it reviews recent advancements in rehabilitation robots, including exoskeletons, end-effector-based robots, and planar robots. The article also evaluates existing upper extremity rehabilitation robots based on their mechanical design and functionality, identifies their limitations, and suggests future research directions for further improvement.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 174-190"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000120/pdfft?md5=a51e80d94f3f2f6ca53c667c4682ef83&pid=1-s2.0-S2667241324000120-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fourier Hilbert: The input transformation to enhance CNN models for speech emotion recognition 傅里叶·希尔伯特：输入变换增强CNN模型的语音情感识别

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.11.002

Bao Long Ly

{"title":"Fourier Hilbert: The input transformation to enhance CNN models for speech emotion recognition","authors":"Bao Long Ly","doi":"10.1016/j.cogr.2024.11.002","DOIUrl":"10.1016/j.cogr.2024.11.002","url":null,"abstract":"<div><div>Signal processing in general, and speech emotion recognition in particular, have long been familiar Artificial Intelligence (AI) tasks. With the explosion of deep learning, CNN models are used more frequently, accompanied by the emergence of many signal transformations. However, these methods often require significant hardware and runtime. In an effort to address these issues, we analyze and learn from existing transformations, leading us to propose a new method: Fourier Hilbert Transformation (FHT). In general, this method applies the Hilbert curve to Fourier images. The resulting images are small and dense, which is a shape well-suited to the CNN architecture. Additionally, the better distribution of information on the image allows the filters to fully utilize their power. These points support the argument that FHT provides an optimal input for CNN. Experiments conducted on popular datasets yielded promising results. FHT saves a large amount of hardware usage and runtime while maintaining high performance, even offers greater stability compared to existing methods. This opens up opportunities for deploying signal processing tasks on real-time systems with limited hardware.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 228-236"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

POMDP-based probabilistic decision making for path planning in wheeled mobile robot 基于 POMDP 的轮式移动机器人路径规划概率决策

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.06.001

Shripad V. Deshpande, Harikrishnan R, Rahee Walambe

{"title":"POMDP-based probabilistic decision making for path planning in wheeled mobile robot","authors":"Shripad V. Deshpande, Harikrishnan R, Rahee Walambe","doi":"10.1016/j.cogr.2024.06.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2024.06.001","url":null,"abstract":"<div><p>Path Planning in a collaborative mobile robot system has been a research topic for many years. Uncertainty in robot states, actions, and environmental conditions makes finding the optimum path for navigation highly challenging for the robot. To achieve robust behavior for mobile robots in the presence of static and dynamic obstacles, it is pertinent that the robot employs a path-finding mechanism that is based on the probabilistic perception of the uncertainty in various parameters governing its movement. Partially Observable Markov Decision Process (POMDP) is being used by many researchers as a proven methodology for handling uncertainty. The POMDP framework requires manually setting up the state transition matrix, the observation matrix, and the reward values. This paper describes an approach for creating the POMDP model and demonstrates its working by simulating it on two mobile robots destined on a collision course. Selective test cases are run on the two robots with three categories – MDP (POMDP with belief state spread of 1), POMDP with distribution spread of belief state over ten observations, and distribution spread across two observations. Uncertainty in the sensor data is simulated with varying levels of up to 10 %. The results are compared and analyzed. It is demonstrated that when the observation probability spread is increased from 2 to 10, collision reduces from 34 % to 22 %, indicating that the system's robustness increases by 12 % with only a marginal increase of 3.4 % in the computational complexity.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 104-115"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000077/pdfft?md5=ccfa806c0ae32c5aba224cbf968b6b8d&pid=1-s2.0-S2667241324000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Food Sample Handling and Placement Pattern Recognition with YOLO: Advanced Techniques in Robotic Object Detection 利用 YOLO 优化食品样品处理和放置模式识别：机器人物体检测的先进技术

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.01.001

Shoki Koga, Keisuke Hamamoto, Huimin Lu, Y. Nakatoh

引用次数: 0

Autonomous novel class discovery for vision-based recognition in non-interactive environments 在非交互式环境中自主发现基于视觉识别的新类别

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.10.002

Xuelin Zhang , Feng Liu , Xuelian Cheng , Siyuan Yan , Zhibin Liao , Zongyuan Ge

引用次数: 0