Search

Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance. [ABSTRACT FROM AUTHOR]

In recent years significant advancements have been made in the field of edge detection in digital images through the integration of various techniques, resulting in the emergence of a novel methodology known as multimodal edge detection. This research paper presents an innovative solution for the identification and localisation of nanoparticles in digital images by leveraging different aspects of digital image processing. The effectiveness of the proposed approach is evaluated by analysing a large number of samples using spatial and amplitude resolution frameworks. Matlab software is used to provide the necessary mathematical support for this study. The results demonstrate notable advancements in the components of edge detection, and the proposed method consistently performs well across different detection scenarios. The findings of this investigation have potential implications for the advancement of edge detection techniques in various application domains. [ABSTRACT FROM AUTHOR]

This paper, by using Borjesson's and Benitez's approximation of Q-function, presents a novel and improved composite approximation of Q-function with wide applicability. The presented approach is very general and can be implemented on any observed interval. Based on the proposed approximation of Q-function, the average bit error rate is assessed by observing the transfer over Nakagami-m fading channel. The simplicity of the proposed approximation form in conjunction with yet another feature - utmost accurateness - appeared to be a better choice than the suggested approximations of similar complexity in terms of analyticity. The paper emphasizes the wide implementation possibilities in numerous tasks of communication theory and functional analysis that include Q-function. [ABSTRACT FROM AUTHOR]

The influence of different wavelet transformations and decomposition on edge detection was examined, using convenient operators to images of various complexities. Berkeley Segmentation Database images with the corresponding ground truth were used. The categorization of those images was accomplished according to the degree of complexity in three groups (small, medium, and large number of details), by using discrete cosine transformation and discrete wavelet transformation. Three levels of decomposition for eight wavelet transformations and five operators for edge detection were applied on these images. As an objective measure of the quality for edge detection, the parameters "performance ratio" and "F-measure" were used. The obtained results showed that edge detection operators behaved differently in images with a different number of details. Decomposition significantly degrades the image, but useful information can be extracted at the third level of decomposition, because the image with a different number of details behaves differently at each level. For an image with a certain number of details, decomposition Level 3 in some cases gives better results than Level 2. The obtained results can be applied to image compression with different complexity. By selecting a certain combination of operators and decomposition levels, a higher compression ratio with preserving a larger amount of useful image information can be achieved. Depending on the image resolution whereby the number of details varies, an operator optimization can be performed according to the decomposition level in order to obtain the best possible edge detection. [ABSTRACT FROM AUTHOR]

In this paper, capitalizing on novely introduced interval approximation of Q-function, we have presented simple and tight approximation for the evaluation of the average symbol error probability (ASEP) over fading channels. First, comparison to other known Q-function closed-form approximations has been performed, and it has been shown that accuracy improvement has been achieved by using proposed interval approximation in whole range of input values. Further, it has been shown that by using proposed approximation, values of ASEP for some applied modulation formats could be efficiently and accurately evaluated when transmission over fading channels is observed. Also has been shown that than by using proposed approximation, ASEP measures are bounded more closely than by using other known Q-function closed-form approximations. [ABSTRACT FROM AUTHOR]