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Go to Editorial ManagerWavelet-based algorithms are increasingly used in the source coding of remote sensing, satellite and other geospatial imagery. At the same time, wavelet-based coding applications are also increased in robust communication and network transmission of images. Although wireless multimedia sensors are widely used to deliver multimedia content due to the availability of inexpensive CMOS cameras, their computational and memory resources are still typically very limited. It is known that allowing a low-cost camera sensor node with limited RAM size to perform a multi-level wavelet transform, will in return limit the size of the acquired image. Recently, fractional wavelet filter technique became an interesting solution to reduce communication energy and wireless bandwidth, for resource-constrained devices (e.g. digital cameras). The reduction in the required memory in these fractional wavelet transforms is achieved at the expense of the image quality. In this paper, an adaptive fractional artifacts reduction approach is proposed for efficient filtering operations according to the desired compromise between the effectiveness of artifact reduction and algorithm simplicity using some local image features to reduce boundaries artifacts caused by fractional wavelet. Applying such technique on different types of images with different sizes using CDF 9/7 wavelet filters results in a good performance.
In recent years, there has been a lot of interest in the study of P300 potential-based approaches for lie detection. The variations in brain signal activity (EEG-P300 component) that distinguish between lying and starting the truth are investigated. As soon as participants respond to an experiment stimulus for the first time, their brain signals are examined and the P300 signal is extracted. This paper aims to improve the signal-to-noise ratio (SNR) of P300, which leads to an increase in the classification accuracy of lie detection. Ten subjects were randomly assigned to groups of lying and innocent people, and 14 electrodes captured the EEG data for each group. This work proposed to use some denoising techniques like averaging the raw EEG signal, regression-based baseline correction, and independent component analysis (ICA). The suggested approach and other early published methods vary mostly in the regression-based technique used in bassline correction to adaptively indicate the baseline interval (baseline window). Compared to other studies, the suggested technique gives an increase in the mean amount of SNR by up to 20% was obtained.
Low-quality data can be dangerous for the machine learning models, especially in crucial situations. Some large-scale datasets have low-quality data and false labels, also, datasets with images type probably have artifacts and biases from measurement errors. So, automatic algorithms that are able to recognize low-quality data are needed. In this paper, Shapley Value is used, a metric for evaluation of data, to quantify the value of training data to the performance of a classification algorithm in a large ImageNet dataset. We specify the success of data Shapley in recognizing low-quality against precious data for classification. We figure out that model performance is increased when low Shapley values are removed, whilst classification model performance is declined when high Shapley values are removed. Moreover, there were more true labels in high-Shapley value data and more mislabeled samples in low-Shapley value. Results represent that mislabeled or poor-quality images are in low Shapley value and valuable data for classification are in high Shapley value.
Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complex- valued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.