An electroencephalogram (EEG) signal is a dominant indicator of brain activity that contains conspicuous information about the underlying mental state. The EEG signals classification is desirable in order to comprehend the objective behavior of the brain in various diseased or control activities. Even though many studies have been done to find the best analytical EEG system, they all focus on domain-specific solutions and can't be extended to more than one domain. This study introduces a multidomain adaptive broad learning EEG system (MABLES) for classifying four different EEG groups under a single sequential framework. In particular, this work expands the applicability of three previously proposed modules, namely, empirical Fourier decomposition (EFD), improved empirical Fourier decomposition (IEFD), and multidomain features selection (MDFS) approaches for the realization of MABLES. The feed-forward neural network classifier is used in extensive trials on four different datasets utilizing a 10-fold cross-validation technique. Results compared to previous research show that the mental imagery, epilepsy, slow cortical potentials, and schizophrenia EEG datasets have the highest average classification accuracy, with scores of 94.87%, 98.90%, 92.65% and 95.28%, respectively. The entire qualitative and quantitative study verifies that the suggested MABLES framework exceeds the existing domain-specific methods regarding classification accuracies and multi-role adaptability, therefore can be recommended as an automated real-time brain rehabilitation system.
PurposeOptical coherence tomography (OCT) is a noninvasive, high-resolution imaging modality capable of providing both cross-sectional and three-dimensional images of tissue microstructures. Owing to its low-coherence interferometry nature, however, OCT inevitably suffers from speckles, which diminish image quality and mitigate the precise disease diagnoses, and therefore, despeckling mechanisms are highly desired to alleviate the influences of speckles on OCT images.ApproachWe propose a multiscale denoising generative adversarial network (MDGAN) for speckle reductions in OCT images. A cascade multiscale module is adopted as MDGAN basic block first to raise the network learning capability and take advantage of the multiscale context, and then a spatial attention mechanism is proposed to refine the denoised images. For enormous feature learning in OCT images, a deep back-projection layer is finally introduced to alternatively upscale and downscale the features map of MDGAN.ResultsExperiments with two different OCT image datasets are conducted to verify the effectiveness of the proposed MDGAN scheme. Results compared those of the state-of-the-art existing methods show that MDGAN is able to improve both peak-single-to-noise ratio and signal-to-noise ratio by 3 dB at most, with its structural similarity index measurement and contrast-to-noise ratio being 1.4% and 1.3% lower than those of the best existing methods.ConclusionsResults demonstrate that MDGAN is effective and robust for OCT image speckle reductions and outperforms the best state-of-the-art denoising methods in different cases. It could help alleviate the influence of speckles in OCT images and improve OCT imaging-based diagnosis.
Conformal coating is a thin film used for protecting printed circuit boards (PCBs) from harsh environmental conditions, which reduces the failure rate of PCBs. The thickness of conformal coating is one of the key factors determining the protection efficacy on PCB. Therefore, the thickness measurement is highly desired to qualify the conformal coating. In this study, we propose to employ high-resolution spectral-domain optical coherence tomography (SD-OCT) for measuring the conformal coating thickness. An SD-OCT with axial resolution of 1.72 μm is developed. The system can provide cross-sectional imaging of the conformal coating layer. Then a boundary detection algorithm is developed to identify the coating layer from the OCT image and eventually calculate the thickness of the coating layer. Our proposed method is evaluated through comparing with metallographic slicing method, which cuts PCB into cross-section and measure conformal coating thickness under a microscope. The results demonstrate that our method produces a very consistent measurement results as compared to metallographic slicing method. In addition to the good accuracy, our algorithm’s computation load is low (about one hundred milliseconds per B-scan), indicating the potential to achieve on-line inspection of coating thickness.
As an emerging technique capable of providing cellular/subcellular-level tissue microstructure images, optical coherence tomography (OCT) is regarded to be a viable tool for early disease diagnosis, yet few studies on pancreatic imaging have ever been reported in literature. In this paper, we utilized a lab-built micro-OCT (μOCT) for cellular/subcellular pancreatic imaging for both normal tissues and those specimens with edema, and evaluate the feasibility of OCT as an imaging tool for early pancreatic disease diagnosis. Results show that the cellular/subcellular-level pancreatic microstructures of normal tissues could be clearly identified, and is quite different from those in tissues with edema. Such results demonstrate the great potential of μOCT as a viable tool for pancreatic tissue imaging in clinical practice.
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