Detection and Enumeration of Bacterial Pathogens in the American Oyster (Crassostrea virginica)

American oyster (Crassostrea virginica) is a popular seafood for its delicacy and high nutritional value. Based on increasing concern about contamination of bacterial pathogens in raw oyster, my research objectives have been focused on detection and enumeration of two important bacterial pathogens, Escherichia coli and Salmonella spp. in the American oyster in south Texas waters, local markets and controlled laboratory studies. Immunohistochemical and RT-PCR analyses showed substantial bacterial pathogen’s presence in gills and digestive glands of oysters collected from San Martin Lake and South Padre Island as well as local markets. Laboratory studies showed increasing trend of both bacterial pathogens with elevated temperatures (28 and 32 °C) compared to control (24 °C). Extrapallial fluid, an important body fluid, glucose levels, pH, and protein concentration varied in oysters and appeared to be pertinent with pathogen intensity. Collectively, these results suggest that American oyster is prone to water-borne pathogen contamination in south Texas waters

Multi-View Frame Reconstruction with Conditional GAN

Multi-view frame reconstruction is an important problem particularly when multiple frames are missing and past and future frames within the camera are far apart from the missing ones. Realistic coherent frames can still be reconstructed using corresponding frames from other overlapping cameras. We propose an adversarial approach to learn the spatio-temporal representation of the missing frame using conditional Generative Adversarial Network (cGAN). The conditional input to each cGAN is the preceding or following frames within the camera or the corresponding frames in other overlapping cameras, all of which are merged together using a weighted average. Representations learned from frames within the camera are given more weight compared to the ones learned from other cameras when they are close to the missing frames and vice versa. Experiments on two challenging datasets demonstrate that our framework produces comparable results with the state-of-the-art reconstruction method in a single camera and achieves promising performance in multi-camera scenario.Comment: 5 pages, 4 figures, 3 tables, Accepted at IEEE Global Conference on Signal and Information Processing, 201

Pengaruh Jumlah Industri dan Tenaga Kerja Terhadap Nilai Produksi Industri Formal Kecil

The purpose of this study to analyze the effect of the number of industries and labor on the value of production in small industries in Lamongan Regency using the Durbin-Watson test. The Durbin-Watson test is an autocorrelation test that assesses the presence of autocorrelation in residuals. Data taken from the Central Bureau of Statistics of Lamongan Regency. Furthermore, it was analyzed using SPSS assistance. Based on the results of the analysis, it is known that there is no autocorrelation between the number of industries and workers on the value of production in small industries in Lamongan Regency

Calibration of Multi-LIDAR Systems for Real-Time Surface Management: Application in Bucket Wheel Reclaimer

Stockpile reclaiming using a Bucket Wheel Reclaimer (BWR) is an important part of stockyard management. The growth in demand for material handling over the years has drawn attention to improve the automation of the process. However, studies have shown that stockpiled products are being reclaimed at approximately 50% of their potential. This study focuses on the challenges in the automation of stockyard management system using a BWR.For high accuracy point cloud computation and surface reconstruction of the stockpiled materials, accurate calibration is of crucial importance. This dissertation presents a calibration technique for multi-LIDAR systems to estimate the GNSS-LIDAR extrinsic parameters of BWR's. The approach presented works with one or more 2D LIDARs and does not require special markers (e.g., reflective tape) or surveyed locations (other than a DGNSS base station antenna). The method and its accuracy have been demonstrated using experimental data from a stockyard environment.Regarding real-time management and control, the dissertation presents a technique for real-time point cloud management, visualization, and feature extraction for large scale stockyards environments. The software solution described continuously manages the point cloud in real-time as the sensors stream data. It also displays the current stockpile on an interactive interface that allows the user to see the surface from different view-points, interrogate the coordinates of any surface location, and computes the BWR entry and exit point for automated operation. The software is tested using experimental data from a port located in Yantai, China

Challenges in Partially-Automated Roadway Feature Mapping Using Mobile Laser Scanning and Vehicle Trajectory Data

Connected vehicle and driver's assistance applications are greatly facilitated by Enhanced Digital Maps (EDMs) that represent roadway features (e.g., lane edges or centerlines, stop bars). Due to the large number of signalized intersections and miles of roadway, manual development of EDMs on a global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the preferred data acquisition method to provide data for automated EDM development. Such systems provide an MTLS trajectory and a point cloud for the roadway environment. The challenge is to automatically convert these data into an EDM. This article presents a new processing and feature extraction method, experimental demonstration providing SAE-J2735 map messages for eleven example intersections, and a discussion of the results that points out remaining challenges and suggests directions for future research.Comment: 6 pages, 5 figure

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%

Factors Affecting Academic Performance of Undergraduate Students at International Islamic University Chittagong (IIUC), Bangladesh

This study was conducted to examine different factors influencing socio-economic background and the academic performance of undergraduate students enrolled at International Islamic University Chittagong (IIUC) with a view to assessing their individual performances and improvements. The assessment covers comparative achievements of different faculties of IIUC-Shariah & Islamic Studies, Business Administration, Science & Engineering, Arts &Humanities and Laws. The data were collected from 200 undergraduate students from different faculties of International Islamic University Chittagong (IIUC) through separate structured questionnaire using the simple random sampling technique. For analysis, simple percentage and linear regression model were run to evaluate comparative importance of the factors. The result shows that over all CGPA of IIUC student is 3.25 (Out of 4.00). Regression results of academic performance of students have varied from faculty to faculty. The result also reveals that age, gender, past academic track, medium of education and absence in the classes have also influenced the academic performances of a student. The study has covered the period of academic year Autumn-2013 to Spring-2014. Keywords: Socio-economic Background, Regression, Faculties, IIUC, Bangladesh