Wireless communication systems face many challenges due to fluctuating channel conditions resulting in variable error rates and demands robust error management tactics. Traditional Automatic Repeat Request (ARQ) metho...
详细信息
Early diagnosis of cardiac abnormalities plays a crucial role in preventing severe cardiovascular diseases. This paper presents a novel approach for detecting and classifying small objects, such as anomalies, in cardi...
详细信息
ISBN:
(纸本)9798350354218
Early diagnosis of cardiac abnormalities plays a crucial role in preventing severe cardiovascular diseases. This paper presents a novel approach for detecting and classifying small objects, such as anomalies, in cardiac images to facilitate early diagnosis and intervention. The proposed methodology integrates various image processing and machine learning techniques, including input image preprocessing, edge detection, boundary extraction, KAZE feature extraction, region mapping, morphological analysis, ensemble learning, and convolutional neural network (CNN) classification, followed by decision-making mechanisms. Initially, the input cardiac images undergo preprocessing to enhance quality and reduce noise, followed by edge detection to identify potential regions of interest. Subsequently, boundary extraction techniques are applied to delineate object boundaries for more accurate analysis. KAZE feature extraction is then employed to capture discriminative features from the identified regions. Next, a region mapping approach is utilized to segment and classify small objects within the cardiac images. Morphological analysis is applied to refine the detected regions and improve classification accuracy. An ensemble learning method is then employed to integrate diverse classifiers for enhanced performance. Furthermore, a CNN classifier is trained on the extracted features to classify the detected objects into relevant categories, facilitating automated diagnosis. Finally, a decision-making mechanism is employed to interpret the classification results and provide actionable insights for healthcare professionals. The proposed approach offers a robust solution for early diagnosis of cardiac abnormalities by effectively detecting and classifying small objects in cardiac images. Experimental results demonstrate the efficacy of the proposed methodology in improving diagnostic accuracy and efficiency, thereby contributing to enhanced patient care and prognosis in cardiovascular
The Internet of Vehicles (IoV) has become one challenging communication technology in the current internet world. IoV enables real-time data exchange between vehicles, road infrastructures, and mobile communication de...
详细信息
This paper presents a fuzzy clustering algorithm by incorporating fuzzy entropy for segmentation of 3D brain magnetic resonance (MR) images. Intensity inhomogeneity (IIH) and noise strongly affect brain MR images beca...
详细信息
Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependenc...
详细信息
Hyperparameter optimization poses a significant challenge when developing deep neural networks. Building a convolutional neural network (CNN) for implementation can be an arduous and time-intensive task. This work pro...
详细信息
In the last three decades, a lot of work has been done for building Automatic Speech Recognition (ASR) systems for well-established languages such as English, Chinese, etc. However, for implementing a Large Vocabulary...
详细信息
Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system co...
详细信息
Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.
This paper gives deeper insight into the range of recent approaches developed and reported in the literature specifically for monophonic acoustic event classification (AEC), polyphonic acoustic event detection (AED) a...
详细信息
Many applications widely use broadcast communications (BC) due to their efficiency in simultaneously distributing data to many receivers. More specifically, BC is essential in resource-constrained devices (RCDs) for c...
详细信息
暂无评论