The Plasma-MAG (Metal Active Gas) welding process represents an advanced hybrid welding technique that combines the precision of plasma arc welding with the robust shielding capabilities of MAG welding. This integrati...
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Browsing online resources is crucial for detecting activities like criminal or anomalous behavior, but it involves analyzing vast amounts of data. Big data tools are essential for this task. This paper presents AISear...
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Karyotyping is a study of chromosomes to identify various chromosomal aberrations related to structure and number. Chromosome image analysis involves challenging issues related to overlapping and touching of chromosom...
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Karyotyping is a study of chromosomes to identify various chromosomal aberrations related to structure and number. Chromosome image analysis involves challenging issues related to overlapping and touching of chromosomes. Chromosome segmentation and classification generally focus on separating overlapping and touching chromosomes. The analysis methods start from conventional image processing methods to advanced machine learning techniques. These methods are broadly classified into low-level and high-level methods. The low-level methods are thresholding-based approaches, edge detection, feature extraction techniques like active contours and watershed approaches and machine learning for classification. The high-level methods are deep learning algorithms like convolutional neural networks (CNNs), U-Net, autoencoder architectures. These methods help in improving accuracy and automate the process of chromosome segmentation and classification. High-level approaches can handle complexity in chromosome overlaps which provides better segmentation results. The approach learns complicated patterns and structures of chromosome images, which helps in achieving better classification accuracy. The challenges are: (i) working on large and annotated dataset for training deep learning models and (ii) suffer issues with new dataset even in they perform better during training phase. The solution for all these can be a hybrid approach that combines conventional method with modern approaches. This survey gives readers a basic understanding of automated karyotyping and future direction in this domain.
This study presents advanced neural network architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for en...
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The proceedings contain 33 papers. The topics discussed include: enhancing image security using legacy-based encryption with chaotic tent map and memristor;segmentation of retina vessels in 2D OCT-reconstructed fundus...
ISBN:
(纸本)9798350304985
The proceedings contain 33 papers. The topics discussed include: enhancing image security using legacy-based encryption with chaotic tent map and memristor;segmentation of retina vessels in 2D OCT-reconstructed fundus images with 3D UNet;advanced fault diagnosis in rotating machines using 2D grayscale images with improved deep convolutional neural networks;automated audio time alignment for multi-microphone setups: an open-source approach;analog, programmable switched capacitor FIR filter based on rotator architecture implemented in CMOS technology;contour extraction of surgical stoma surfaces using 2.5D images from smartphone 3D scanning;convolutional transformer-based image compression;fast prototyping of in-pavement airport navigation lamp prism classification;diagnostic development of damage of ship generator sets by means of electrical signals;and memory-efficient graph convolutional networks for object classification and detection with event cameras.
The aim of this research is to develop a method for prediction of left ventricular recovery one year after myocardial infarction using texture parameters estimated for static ultrasound images. The study is performed ...
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ISBN:
(纸本)9781509026616
The aim of this research is to develop a method for prediction of left ventricular recovery one year after myocardial infarction using texture parameters estimated for static ultrasound images. The study is performed for the monochrome and color (contrast based) echocardiograms that allow advanced evaluation of myocardial function. The analysis includes investigation of different texture feature selection methods and application of multilayer neural network, Support Vector Machine, and decision tree based Adaboost algorithm for recovery prediction. The obtained preliminary results are promising;estimated prediction error is lower than 30% for all used prediction tools.
The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is ...
ISBN:
(纸本)9781447159209
The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis; Part ii deals with techniques and models of computational intelligence that are suitable for signalprocessing; andPart iiI details ECG system-diagnostic interpretation and knowledge acquisition architectures. Illustrative material includes: brief numerical experiments; detailed schemes, exercises and more advanced problems.
The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is ...
ISBN:
(纸本)0857298674
The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis; Part ii deals with techniques and models of computational intelligence that are suitable for signalprocessing; andPart iiI details ECG system-diagnostic interpretation and knowledge acquisition architectures. Illustrative material includes: brief numerical experiments; detailed schemes, exercises and more advanced problems.
Previously, most mammalian auditory systems research has concentrated on human sensory perception whose frequencies are lower than 20 kHz. The implementations almost always used analog VLSI design. Due to the complexi...
Previously, most mammalian auditory systems research has concentrated on human sensory perception whose frequencies are lower than 20 kHz. The implementations almost always used analog VLSI design. Due to the complexity of the model, it is difficult to implement these algorithms using current digital technology. This paper introduces a simplified model of biosonic reception system in bats and its implementation in the ‘‘Chiroptera Inspired Robotic CEphaloid’’ (CIRCE) project. This model consists of bandpass filters, a half‐wave rectifier, low‐pass filters, automatic gain control, and spike generation with thresholds. Due to the real‐time requirements of the system, the system employs Butterworth filters and advanced field programmable gate array (FPGA) architectures to provide a viable solution. The ultrasonic signalprocessing is implemented on a Xilinx FPGA Virtex ii device in real time. In the system, 12‐bit input echo signals from receivers are sampled at 1 M samples per second for a signal frequency range from 20 to 200 kHz. The system performs a 704‐channel per ear auditory pipeline operating in real time. The output of the system is a coded time series of threshold crossing points. Comparing hardware implementation with fixed‐point software, the system shows significant performance gains with no loss of accuracy.
For single-input multiple-output (SIMO) systems blind deconvolution based on second-order statistics has been shown promising given that the sources and channels meet certain assumptions. In our previous paper we exte...
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ISBN:
(纸本)0819432938
For single-input multiple-output (SIMO) systems blind deconvolution based on second-order statistics has been shown promising given that the sources and channels meet certain assumptions. In our previous paper we extend the work to multiple-input multiple-output (MIMO) systems by introducing a blind deconvolution algorithm to remove all channel dispersion followed by a blind decorrelation algorithm to separate different sources from their instantaneous mixture. In this paper we first explore more details embedded in our algorithm. Then we present simulation results to show that our algorithm is applicable to MIMO systems excited by a broad class of signals such as speech, music and digitally modulated symbols.
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