Detection of Structure Characteristics and Its Discontinuity Based Field Programmable Gate Array Processor in Cancer Cell by Wavelet Transform

Digital clinical histopathology is one of the crucial techniques for precise cancer cell diagnosing in modern medicine. The Synovial Sarcoma (SS) cancer cell patterns seem to be a spindle shaped cell (SSC) structure and it is very difficult to identify the exact oval shaped cell structure through pathologist’s eye perception. Meanwhile, there is necessitating for monitoring and securing the successful and effective image data processing in the the huge network data which is also a complex one. A field programmable Gate Array (FPGA) was regarded as a necessary one for this. In this work, based on FPGA a Cancer Cell classification is made for the regulation and execution. Hence, mathematically the SSC regularity structures and its discontinuities are measured by the holder exponent (HE) function. In this research work, HE values have been determined by Wavelet Transform Modulus Maxima (WTMM) and Wavelet Leader (WL) methods with basis function of Haar wavelet based on FPGA Processor. The quantitative parameters such as Mean of Asymptotic Discontinuity (MAD), Mean of Removable Discontinuity (MRD) and Number of Discontinuity Points (NDPs) have been considered to determine the prediction of discontinuity detection between WTMM and WL methods. With the help of receiver operating characteristics (ROC) curve, the significant difference of discontinuity detection performance between both the methods has been analyzed. From the experimental results, it is clear that the WL method is more practically feasible and it gives satisfactory performance, in terms of sensitivity and specificity percentage values, which are 80.56% and 59.46%, respectively in the blue color components of the SNR 20 dB noise image.

Intelligent Interpretation of the Geometric Properties of Rock Mass Discontinuities Based on an Unmanned Aerial Vehicle

The geometric properties of rock mass discontinuities are essential for the evaluation of the safety of rock masses. Numerous studies have recently been performed on the extraction of discontinuity information. However, most methods are characterized by poor data collection and processing efficiency. This paper presents a UAV-based methodology for the accurate and complete acquisition of rock surface data, as well as the automatic extraction of discontinuity information. Moreover, a program called Random Sample Consensus (RANSAC) Discontinuity Detection (RDD) is developed to extract discontinuity information based on the proposed method. The conclusions of this research are as follows. 1) RANSAC Discontinuity Detection (RDD) can identify the feature point set of discontinuities from a raw point cloud, and can calculate the discontinuity orientation. 2) The boundary of a discontinuity can be precisely depicted using the improved Graham scan algorithm. 3) The orientations of marked discontinuities extracted by RDD are compared with those extracted by the three-point method in CloudCompare. The differences in the orientations extracted by the two methods are found to be less than 3° for flat discontinuities and only about 4.87° for rough discontinuities, which are within a reasonable error range in practical engineering applications. Therefore, the feasibility of the proposed method is verified.

Multidirectional eigenvalue-based coherence attribute for discontinuity detection

The conventional coherence attribute is typically applied to migrated full-stacked seismic data volumes to detect geological discontinuities. Recently, multispectral, multiazimuth, and multioffset coherence attributes have been proposed and implemented with different seismic data volumes of specific frequencies, azimuths, and offsets to enhance discontinuities. Generally, geological anomalies, such as faults and channels, will be better illuminated by a perpendicular rather than a parallel direction for computation. Therefore, we propose a multidirectional eigenvalue-based coherence attribute by establishing multiple covariance matrices along certain different directions on a single post-stack volume. We adopt two methods to compute multidirectional coherence attribute. One is to compute multiple coherence volumes in different directions and to define the minimum as the final multidirectional coherence. This method is time-consuming, but could provide partial and overall discontinuity simultaneously. The other method obtains one coherence volume by summing covariance matrices in different directions, which is computationally efficient, but only provides overall discontinuity. The performance of 3D physical model and field data volumes demonstrates that multidirectional coherence can highlight subtle geologic structures with a higher resolution than conventional coherence. This suggests that multidirectional coherence attribute may serve as an effective tool for detecting the distribution of geologic discontinuities in seismic interpretation.

Automated Discontinuity Detection and Reconstruction in Subsurface Environment of Mars Using Deep Learning: A Case Study of SHARAD Observation

Machine learning (ML) algorithmic developments and improvements in Earth and planetary science are expected to bring enormous benefits for areas such as geospatial database construction, automated geological feature reconstruction, and surface dating. In this study, we aim to develop a deep learning (DL) approach to reconstruct the subsurface discontinuities in the subsurface environment of Mars employing the echoes of the Shallow Subsurface Radar (SHARAD), a sounding radar equipped on the Mars Reconnaissance Orbiter (MRO). Although SHARAD has produced highly valuable information about the Martian subsurface, the interpretation of the radar echo of SHARAD is a challenging task considering the vast stocks of datasets and the noisy signal. Therefore, we introduced a 3D subsurface mapping strategy consisting of radar echo pre-processors and a DL algorithm to automatically detect subsurface discontinuities. The developed components the of DL algorithm were synthesized into a subsurface mapping scheme and applied over a few target areas such as mid-latitude lobate debris aprons (LDAs), polar deposits and shallow icy bodies around the Phoenix landing site. The outcomes of the subsurface discontinuity detection scheme were rigorously validated by computing several quality metrics such as accuracy, recall, Jaccard index, etc. In the context of undergoing development and its output, we expect to automatically trace the shapes of Martian subsurface icy structures with further improvements in the DL algorithm.