Experimental Study on Sampling Theorem in Signal Processing

This practicum is to define the study properties of the sampling theorem. Understand the effect of selecting the sample size and its effect on the signal recovery process. The experiment utilizes a computer or portable workstation to run an examination of the hypothesis reenactment program. From the test information gotten, it can be concluded that the more noteworthy the frequency of the signal to be inspected, the closer the signal will be to the initial signal. The time and frequency of the examining signal are conversely relative. The higher the frequency, the lower the time will be. The magnitude of the amplitude of the output signal is indeterminate

Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation

We propose a novel approach to jointly perform 3D shape retrieval and pose estimation from monocular images.In order to make the method robust to real-world image variations, e.g. complex textures and backgrounds, we learn an embedding space from 3D data that only includes the relevant information, namely the shape and pose. Our approach explicitly disentangles a shape vector and a pose vector, which alleviates both pose bias for 3D shape retrieval and categorical bias for pose estimation. We then train a CNN to map the images to this embedding space, and then retrieve the closest 3D shape from the database and estimate the 6D pose of the object. Our method achieves 10.3 median error for pose estimation and 0.592 top-1-accuracy for category agnostic 3D object retrieval on the Pascal3D+ dataset, outperforming the previous state-of-the-art methods on both tasks

GROUND MOTION IN YOGYAKARTA CITY, YOGYAKARTA SPECIAL PROVINCE, INDONESIA ON DENSELY MICROTREMOR OBSERVATIONS AND SHEAR WAVE VELOCITY

Microtremor is currently considered the foremost tool in site effect studies. The ground motion is estimated with microtremor observations, meaning that subsoil mechanical properties and geometry are evaluated and from them an estimate of local amplification is computed. Here, the ground motion is studied by the site effects of seismic hazard zonation of urban areas in Yogyakarta City. The main purpose of this paper is zoning the geological engineering features and assessing seismic of the research urban area. In this regard, the microtremors are measured at 274 sites by single station sampling method and Nakamura technique. The microtremors of all over the city are processed by a model of Mitutoyo-GPL-6A3P. The amplification factor generally ranges between 0.70 and 5.56 and the natural frequency normally varies between 0.40 and 3.30 Hz. The information layers are prepared in GMT used for detecting the zonation of potential seismic hazard. The shear wave velocity is calculated in 12 existing drilling sites based on the geotechnical approach of SPT for soil condition. To study the ground motion, geological engineering condition is investigated using amplification factor, natural frequency, shear wave velocity maps which are analyzed using densely single microtremor observation and SPT from existing drilling sites. Keywords: Ground motion, amplification factors, natural frequency; H/V spectral ratio, microtremor observations, Yogyakarta Urba

Neural Sparse Voxel Fields

Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detailed appearance and geometry models. Recent studies have demonstrated promising results by learning scene representations that implicitly encode both geometry and appearance without 3D supervision. However, existing approaches in practice often show blurry renderings caused by the limited network capacity or the difficulty in finding accurate intersections of camera rays with the scene geometry. Synthesizing high-resolution imagery from these representations often requires time-consuming optical ray marching. In this work, we introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering. NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree to model local properties in each cell. We progressively learn the underlying voxel structures with a differentiable ray-marching operation from only a set of posed RGB images. With the sparse voxel octree structure, rendering novel views can be accelerated by skipping the voxels containing no relevant scene content. Our method is typically over 10 times faster than the state-of-the-art (namely, NeRF(Mildenhall et al., 2020)) at inference time while achieving higher quality results. Furthermore, by utilizing an explicit sparse voxel representation, our method can easily be applied to scene editing and scene composition. We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering. Code and data are available at our website: https://github.com/facebookresearch/NSVF.Comment: 20 pages, in progres

Burnout Among House Officers in Myanmar: A cross-sectional study

Burnout can result in in a serious negative impact on a doctor's life, the quality of patient care, and the healthcare organization. This study aims to determine the prevalence of burnout and factors affecting burnout among the house officers in Myanmar

Microbiome dataset from the upper respiratory tract of patients living with HIV, HIV/TB and TB from Myanmar

This article contains microbiome data from the upper respiratory tract of patients living with HIV/TB, HIV and TB from Meiktila, a town in Myanmar where there is a high incidence of HIV and TB. Microbiomes were compared for HIV/TB infected and healthy adults from the same population. We collected nasopharyngeal and oropharyngeal swabs from a total of 33 participants (Healthy {5}, HIV/TB {8}, HIV {14}, and TB {6}). DNA was extracted from the swabs and subjected to custom single step 16s rRNA sequencing on an Illumina MiSeq platform. The sequencing data is available via http://www.ncbi.nlm.nih.gov/bioproject/ PRJNA432583

Matryoshka Peek: Toward Learning Fine-Grained,Robust, Discriminative Features for Product Search

10.1109/TMM.2017.2655422IEEE Transactions on Multimedia1961272 - 128