Detecting cardiac arrhythmias is a significant public health concern, but manual identification and classification of arrhythmias using electrocardiogram (ECG) signals is a challenging task. In this paper, we proposed...
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ISBN:
(纸本)9798350351491;9798350351484
Detecting cardiac arrhythmias is a significant public health concern, but manual identification and classification of arrhythmias using electrocardiogram (ECG) signals is a challenging task. In this paper, we proposed an automatic arrhythmia classification system using Machine Learning and Deep Learning algorithms. The proposed pipeline used a wavelet transform technique to eliminate signal noise. Morphological and statistical features were extracted to classify ECG signal beats into five classes: normal, ventricular, supraventricular, fusion, and paced beats. The system was evaluated on the MIT-BIH arrhythmia database using two classification algorithms, SVM and CNN, achieving accuracy rates of 99.34% and 99.72%, respectively.
This study presents a novel non-invasive method for detecting and classifying neuro-degenerative diseases such as Parkinson's (PD) and Alzheimer's (AD) through automatic speech analysis and artificial intellig...
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ISBN:
(纸本)9798350351491;9798350351484
This study presents a novel non-invasive method for detecting and classifying neuro-degenerative diseases such as Parkinson's (PD) and Alzheimer's (AD) through automatic speech analysis and artificial intelligence. The analysis of the voice recordings was carried out using different parametric extraction methods based on the MFCC and prosodic coefficients (VOT, Jitter, Shimmer, HNR, ... ) followed by a classification step based on CNN and FC-DNN neural network. These methods made it possible to extract relevant speech parameters and use them for training and classification. The results obtained showed vocal disturbances in mild and preclinical stages of PD and AD such as articulation, prosody and rhythmic abilities. Developed machine learning algorithms were able to detect subjects with PD with 98% accuracy from rapid syllable repetitions and 96% accuracy for subjects with AD from voice parameters.
In this work, we demonstrate that the ptychographic phase problem can be solved in a live fashion during scanning, while data is still being collected. We propose a generally applicable modification of the widespread ...
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ISBN:
(纸本)9798350344868;9798350344851
In this work, we demonstrate that the ptychographic phase problem can be solved in a live fashion during scanning, while data is still being collected. We propose a generally applicable modification of the widespread projection-based algorithms such as Error Reduction (ER) and Difference Map (DM). This novel variant of ptychographic phase retrieval enables immediate visual feedback during experiments, reconstruction of arbitrary-sized objects with a fixed amount of computational resources, and adaptive scanning. By building upon the Real-Time Iterative Spectrogram Inversion (RTISI) family of algorithms from the audio processing literature, we show that live variants of projection-based methods such asDMcan be derived naturally and may even achieve higher-quality reconstructions than their classic non-live counterparts with comparable effective computational load.
Previously published results demonstrated that a sequential combination of adaptive linear prediction configuration (LPC), adaptive noise cancellation (ANC), and adaptive comb filtering (CF) can effectively remove mat...
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ISBN:
(数字)9798350354058
ISBN:
(纸本)9798350354065
Previously published results demonstrated that a sequential combination of adaptive linear prediction configuration (LPC), adaptive noise cancellation (ANC), and adaptive comb filtering (CF) can effectively remove maternal interference from noninvasive fetal ECGs. More recent research results have verified that the bio-inspired Lévy Flight Firefly Algorithm (LFFA) can be effectively applied to many different forms of linear and non-linear adaptive filter structures, including low sensitivity IIR adaptive structures that could not be used with steepest descent adaptive algorithms due to their adaptive multimodal characteristics. This paper presents results when using the LFFA algorithm in various stages of the FECG sequential combination processing.
This study used a multi-pronged approach to examine how Gulf region Twitter users felt about COVID-19 trending topics. The data collection phase of the study started with the selection of relevant hashtags and time fr...
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ISBN:
(纸本)9798350351491;9798350351484
This study used a multi-pronged approach to examine how Gulf region Twitter users felt about COVID-19 trending topics. The data collection phase of the study started with the selection of relevant hashtags and time frames for analysis. The Twitter API was then used to compile a representative dataset of Arabic tweets pertaining to the COVID-19 trending topics. Subsequently, the research proceeded to the data annotation stage, employing a hybrid annotation technique that fused the transfer learning model and lexicon-based approach to assign a sentiment label to every tweet. Analyzing the patterns of tweet distribution over time exposed interesting patterns and possible sentiment expression influencers. The research obtained good accuracy scores by using a sentiment analysis model that combined three popular machine learning algorithms (Multinomial Naive Bayes, CountVectorizer, and TfidfVectorizer) with three feature representations (Ngram, TfidfVectorizer, and CountVectorizer). The sentiment tendencies of Arabic-speaking Twitter users toward trending topics were revealed by these scores. With the Ngram(1,2) representation, the LinearSVC algorithm achieved an impressive accuracy score of 89.1%, making it stand out as the best performer among all feature representations.
Direction of Arrival (DOA) estimation remains a pivotal aspect in the field of signalprocessing, finding applications across diverse domains. This paper introduces an innovative approach to DOA estimation by integrat...
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ISBN:
(纸本)9798350361261;9798350361278
Direction of Arrival (DOA) estimation remains a pivotal aspect in the field of signalprocessing, finding applications across diverse domains. This paper introduces an innovative approach to DOA estimation by integrating adaptive beamforming techniques. We start by exploring the adaptive array theory, focusing on pattern synthesis and beamforming algorithms to enhance signal clarity and direction detection. The paper then delves into the application of Machine Learning (ML) algorithms for dynamic environment adaptation and improved accuracy in DOA estimation. Our approach contrasts the conventional methods by leveraging adaptive beamforming combined with ML, resulting in not only enhanced estimation accuracy but also in adaptability to varying signal conditions. The paper presents comparative analyses with traditional methods, demonstrating the potential of this integrated approach in complex signal environments.
This paper addresses two key limitations in existing Image signalprocessing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle thes...
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ISBN:
(纸本)9798350344868;9798350344851
This paper addresses two key limitations in existing Image signalprocessing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle these issues, we propose a novel, trainable ISP framework that incorporates both the strengths of traditional ISP techniques and advanced MultiScale Retinex (MSR) algorithms for night-time enhancement. Our method consists of three primary components: an ISP-based Luminance Harmonization layer to initially optimize luminance levels in RAW data, a deep learning-based MSR layer for nuanced decomposition of image components, and a specialized enhancement layer for both precise, regionspecific luminance enhancement and color denoising. The proposed approach is validated through rigorous experiments on machine vision benchmarks and objective visual quality indicators. Our results demonstrate not only a significant improvement over existing methods but also robust adaptability under diverse lighting conditions. This work offers a versatile ISP framework with promising applications beyond its immediate scope.
Applications of brain-computer interface (BCI) systems have grown in importance for assisting individuals with severe motor disabilities in navigating our increasingly technologically dependent society. With applicati...
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ISBN:
(纸本)9798350369298;9798350369304
Applications of brain-computer interface (BCI) systems have grown in importance for assisting individuals with severe motor disabilities in navigating our increasingly technologically dependent society. With applications such as electric wheelchairs and advanced prosthetics in mind, the goal of this research is to develop a system that enables the use of electroencephalographic (EEG) and electromyographic (EMG) signals to control the movement of a robot. An EEG cap was used to obtain occipital alpha power density, frontal muscular artifacts, and sensorimotor mu rhythms, which were then sent back to a PC via Bluetooth for further processing. signal-processingalgorithms and models were developed and implemented to determine the user's mental activity and send signals to the external physical device. The preliminary results from the pilot experiments were very promising. The algorithms will be implemented for real-time signalprocessing and tested with a BCI-controlled robotic device.
During clinical decision-making, the segmentation process Is considered an important task because it provides valuable information about the location, size, and characterization of the brain tumor. However, manual hum...
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ISBN:
(纸本)9798350351491;9798350351484
During clinical decision-making, the segmentation process Is considered an important task because it provides valuable information about the location, size, and characterization of the brain tumor. However, manual human segmentation iserror-prone, time-consuming, and requires skilled neuroradiologists. Magnetic resonance imaging (MRI) could give extremely detailed images for the investigation and diagnosis of glioblastoma brain cancers. We compared evaluated approaches on the BRATS 2021 and BRATS 2022 datasets and found that they outperformed and could compete with state-of-the-art algorithms in comparable settings. In our research, we focused on two crucial tasks: segmentation and MGMT classification. This study also addresses asn objective evaluation through performance evaluation of cutting-edge DL-based techniques for MR image analysis (Brats 2021-Brats 2022). Based on the findings of the contrasted methods, we can confirm that using a combination of DL techniques will produce more accurate segmentation results than depending on a single, unique methodology. For the second task, five distinct deep learning-based methods were evaluated to predict the methylation state of the MGMT promoter.
This paper presents an innovative way of image compression using Field-Programmable Gate Array (FPGA) implementation of the Integer Wavelet Transform (IWT) and Discrete Wavelet Transform (DWT) algorithms. For situatio...
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