Quantitative Identification of Pulse-Like Ground Motions Based on Hilbert–Huang Transform

Aiming to address the problem of pulse-like ground motions being difficult to identify, this paper refines the Baker’s wavelet-based pulse-like ground motions identification method, followed by a new pulse-like ground motion identification method based on Hilbert–Huang Transform (HHT) being proposed. In this method, HHT is used to decompose ground motions instead of wavelet. HHT can overcome the dependence of wavelet analysis on the selection of mother wave, and thus more complex velocity pulses can be identified. In order to compare the effects of two pulse-like ground motion identification methods, HHT-based method and wavelet-based method, respectively, are used to identify ground motions in Pacific Earthquake Engineering Research Center (PEER). After identifying the 3066 groups of ground motions selected from PEER, it is found that the HHT-based method can identify 229 pulse-like ground motions, and the wavelet-based method can identify 150 pulse-like ground motions. More complex shapes of near-fault velocity pulses can be extracted by the HHT-based method. By analyzing the seismic response, fault distance, and cumulative squared velocity (CSV) of these pulse-like ground motions, it is found that the pulse-like ground motions identified by the HHT-based method have strong near-fault characteristics. If a high recognition quality can be guaranteed, the proposed HHT-based method can identify many kinds of near-fault velocity pulses and thus provide more pulse-like ground motions for seismic researches.

Natural Speech Signal Recognition Algorithm

Speech recognition technologies are becoming more and more part of our lives, providing a convenient way to control a variety of electronic devices - voice control. One of the current problems that is solved in the development of such control systems is the problem of insufficient accuracy of voice command recognition. Improvements are being made to increase reliability, independence from individual voice characteristics, and reduce the negative impact of background noise on recognition quality. The paper presents an algorithm for recognizing and processing user intentions using a neural network built on the principle of understanding natural language and processing audio signals for use in the user support system.

Panoptic Segmentation on Panoramic Radiographs: Deep Learning-Based Segmentation of Various Structures Including Maxillary Sinus and Mandibular Canal

Panoramic radiographs, also known as orthopantomograms, are routinely used in most dental clinics. However, it has been difficult to develop an automated method that detects the various structures present in these radiographs. One of the main reasons for this is that structures of various sizes and shapes are collectively shown in the image. In order to solve this problem, the recently proposed concept of panoptic segmentation, which integrates instance segmentation and semantic segmentation, was applied to panoramic radiographs. A state-of-the-art deep neural network model designed for panoptic segmentation was trained to segment the maxillary sinus, maxilla, mandible, mandibular canal, normal teeth, treated teeth, and dental implants on panoramic radiographs. Unlike conventional semantic segmentation, each object in the tooth and implant classes was individually classified. For evaluation, the panoptic quality, segmentation quality, recognition quality, intersection over union (IoU), and instance-level IoU were calculated. The evaluation and visualization results showed that the deep learning-based artificial intelligence model can perform panoptic segmentation of images, including those of the maxillary sinus and mandibular canal, on panoramic radiographs. This automatic machine learning method might assist dental practitioners to set up treatment plans and diagnose oral and maxillofacial diseases.

Сentral serous chorioretinopathy and vitelliform dystrophies occurring in adults: predictors of differential diagnosis

AIM: To study predictors in order to optimize the differential diagnosis of persistent central serous chorioretinopathy (CSCR) and different forms of vitelliform dystrophies occurring in adults. MATERIALS AND METHODS: Ninety eyes of 61 patients with long-term serous retinal detachments were recruited in study. All patients underwent ophthalmologic examination including family history, best corrected visual acuity, biomicroscopy, and multimodal imaging including fundus photo, SD-OCT, OCT-A, BAF, FA, ICGA. After the studies, the patients were divided into two groups: with vitelliform dystrophies 30 eyes of 30 patients and with CSCR 31 eyes of 31 patients. Diagnostic predictors found in both groups were scrutinized, mathematical models were obtained, and their recognition quality was assessed by the area under ROC curve. The criteria for all types of research were studied and the predictive value was assessed with the use of ROC analysis. RESULTS: The most informative non-invasive predictors for the diagnosis of vitelliform dystrophies occurring in adults are the following: a positive family history of the disease, brightness and gradient of hyperautofluorescence (hyperAF), typical hyperAF in the form of a crescent and beads, the presence of massive subretinal deposits and deposits in the form of stalactites. The most informative non-invasive predictors for the diagnosis of persistent CSCR are the following: additional hypoAF or hyperAF points or areas outside the main focus, hyperreflective dots in the neurosensory retina and an increase in choroidal thickness, irregular pigment epithelial detachments, presence of CNV. The highest predictive value for both groups was determined for BAF studies. CONCLUSIONS: The results obtained make it possible to optimize the differential diagnosis of persistent CSCR and different forms of vitelliform dystrophies occurring in adults.

Automated digitization of radial charts

Radial charts were commonly used in the industry to allow retrospective assessment of technological parameters. Today it is relevant to digitize the obtained data in order to simplify the automation of technological processes. Digitization of radial charts by means of standard methods is carried out with the help of human labor at significant time costs. The article proposes an automated method for digitizing radial charts using software, developed in the LabVIEW programming environment. The results of processing radial charts are displayed on the screen in numerical and graphical form, and can also be exported to a file (for example, to Notepad or MS Excel). The presented technique can be applied to images obtained on a color or black-and-white scanner, which minimizes geometric distortions, associated with the conversion of a paper document into electronic form, and ensures recognition quality of the clear plot line with an average relative error of up to 3 %. In case of ink fading or perspective photos of the diagram, the value of relative error can reach 8 %, as a result of which additional manual correction of the data will be required.

Evaluation of the effectiveness of different image skeletonization methods in biometric security systems

Background: Systems of the Internet of Things are actively implementing biometric systems. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results. Methods: The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed. Results: Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has been developed, the recognition results of which have better quality, which allows to increase the recognition quality from 3 to 10%. Conclusion: The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to white if certain conditions are satisfied. This Ateb-Gabor filtering will provide better performance, as it allows to obtain more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the proposed method.

The study of skeleton description reduction in the human fall-detection task

Accurate and reliable real-time fall detection is a key aspect of any intelligent elderly people care system. A lot of modern RGB-D cameras can provide a skeleton description of a human figure as a compact pose presentation. This makes it possible to use this description for further analysis without access to real video and, thus, to increase the privacy of the whole system. The skeleton description reduction based on the anthropometrical characteristics of a human body is proposed. The experimental study on the TST Fall Detection dataset v2 by the Leave-One-Person-Out method shows that the proposed skeleton description reduction technique provides better recognition quality and increases the overall performance of a Fall-Detection System.

Feedback Learning & Assessment: A Mutual Relationship

Assessment is an important tool, mechanism and approach to ensure accreditation, recognition, quality and standardization in any university’s program and courses. A good assessment that is fair, transparent and consistent should be communicated clearly between students and faculty staff to create a learning environment that is conducive to a university setting.