The detection of breathing patterns and inferring one's state (physical, mental, or social well-being) remains a difficult task in the current research community. Moreover, early recognition of abnormal respirator...
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ISBN:
(纸本)9798350363524;9798350363517
The detection of breathing patterns and inferring one's state (physical, mental, or social well-being) remains a difficult task in the current research community. Moreover, early recognition of abnormal respiratory patterns can aid the clinician in early intervention to prevent further deterioration of the patient's condition [1]. In this work, we present a smart and low-cost portable medical device designed for continuous breath monitoring. Our system leverages advanced sensor technology combined with novel signalprocessingalgorithms to achieve high sensitivity and accuracy in detecting breath patterns.
Single-channel blind signal separation (SCBSS) is a critical challenge in signalprocessing, aiming to recover original signal components from a single mixed signal. This technology has broad applications across vario...
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Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG record...
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ISBN:
(纸本)9798331540661;9798331540678
Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. Machine learning has emerged as an asset in EEG signalprocessing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machine learning and EEG signalprocessing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machine learning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machine learning and EEG signalprocessing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.
In recent years, societal changes have led to a growing prominence of pets in people's lives. However, uncontrolled pet reproduction in urban areas has given rise to a significant issue of stray animals, posing se...
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ISBN:
(数字)9781510686731
ISBN:
(纸本)9781510686724
In recent years, societal changes have led to a growing prominence of pets in people's lives. However, uncontrolled pet reproduction in urban areas has given rise to a significant issue of stray animals, posing serious threats to human health and the environment. Conventional manual methods for counting stray animals face challenges in terms of efficiency and the risk of disease transmission. With technological advancements, image recognition, and sound identification, among other techniques, have emerged as crucial tools to address this issue. Image recognition, leveraging intuitive statistics based on external features, combined with the low-power attributes of sound identification and the health assessment capabilities of thermal imaging, collectively provide comprehensive technological support for stray animal population statistics. In the realm of image algorithms, both traditional target detection algorithms and deep learning methods such as RCNN and Faster RCNN employ convolutional neural networks to accurately identify and locate stray animals. Regarding sound algorithms, traditional Gaussian mixture models and hidden Markov models, as well as deep learning techniques involving convolutional neural networks, have effectively enhanced the accuracy of stray animal sound recognition. The integration of image and audio in a hybrid method significantly enhances stray animal monitoring. Employing advanced techniques in video tracking and sound recognition, this approach offers an efficient and practical solution, crucial for wildlife ecosystem surveillance and conservation. Research indicates that the application of deep learning methods in the domains of image and sound has significantly advanced compared to traditional approaches. In terms of image processing, I utilized the YOLO algorithm to perform grid division, feature extraction, and loss computation steps to achieve stray animal detection, demonstrating outstanding performance. Through the application of t
Since human physiological signals (e.g., respiration and heart rate) generate very small Doppler shifts, they are highly susceptible to being masked by strong clutter backgrounds. The main research focus of this proje...
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Improvements in deep learning have benefited signalprocessing on spherical surfaces in particular. This work addresses deep learning for close-range spherical surface signalprocessing. Traditional signalprocessing ...
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In this paper,wavelet transform of vibration signal of CNC machine tool is performed based on Python programming *** transform function in PyWavelets library,the signal is decomposed into sub-signals of different *** ...
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This project mainly studies the hardware architecture of the system, digital signal encoding and decoding processing and digital communication protocol. The composition of hardware architecture needs to address the ad...
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With the rapid development of automobiles towards electrification and intelligence, 5nm Qualcomm 8295 flagship chip has been gradually applied to traditional and new energy vehicles, which brings higher requirements f...
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With the rapid development of satellite communication technology, the emergence of interference signals is becoming more and more frequent, which seriously affects the reliability and safety of communication. For this...
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