3 research outputs found
Rancang Bangun Sistem Pemantau Ketinggian dan Kecepatan Arus Air serta Perekam Kondisi Sungai Berbasis Android
No full textNow a days, the condition of the river has been contaminated by householdwaste especially in the urban areas, the waste comes from people who are not responsible for the trash,so it potentially causes floods. Therefore, this research aims to design a system to monitor water level height and water speed as well as river condition recorder based on android. The use of ultrasonic sensor as an indicator to find out high levels of surface water, the optocoupler sensor as an indicator to know the speed of the water flow and IP cameras as a tool of monitoring the condition of the river are actual and direct. Research on the design of the ultrasonic sensor and optocoupler sensor resulted in average percentage error of measurement of 0.54% and 9.35%. IP camera that was designed as a visualization tool monitoring the condition of the river was able to produce on average the smallest delay 0ms. The notification system was implemented on the device users generating the biggest delay on average 22,9s and an average delay of the smallest 9,6s. QoS analysis on android gained the biggest delay of 1.64ms, the largest throughput of 81.01 Kb/s and packet loss of 0%. While QoS analysis on the website obtained the biggest delay of 58ms, the largest throughput of 19.3 KB/s and packet loss of 0%.Kondisi sungai saat ini telah tercemar oleh sampah rumah tangga terutama di perkotaan, sampah tersebut berasal dari masyarakat yang tidak bertanggung jawab atas sampah, sehingga berpotensi menimbulkan banjir. Oleh karena itu, penelitian ini bertujuan untuk merancang sistem pemantauan ketinggian muka air dan kecepatan air serta pencatatan kondisi sungai berbasis android. Penggunaan sensor ultrasonik sebagai indikator untuk mengetahui ketinggian permukaan air, sensor optocoupler sebagai indikator untuk mengetahui kecepatan aliran air dan kamera IP sebagai alat monitoring kondisi sungai secara aktual dan langsung. Penelitian perancangan sensor ultrasonik dan sensor optocoupler menghasilkan persentase kesalahan rata-rata pengukuran sebesar 0,54% dan 9,35%. Kamera IP yang dirancang sebagai alat visualisasi pemantauan kondisi sungai mampu menghasilkan delay paling kecil rata-rata 0ms. Sistem notifikasi yang diimplementasikan pada perangkat pengguna menghasilkan delay terbesar rata-rata 22,9s dan delay rata-rata paling kecil 9,6s. Analisis QoS pada android didapatkan delay terbesar 1,64ms, throughput terbesar 81,01 Kb / s dan packet loss 0%. Sedangkan analisis QoS pada website didapatkan delay terbesar 58ms, throughput terbesar 19,3 KB / s dan packet loss 0%
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
No full textWelcome to the the CSAW-M dataset homepageThis page includes the files and metadata related to the CSAW-M, a curated dataset of mammograms with expert assessments of the masking of cancer.
CSAW-M is collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We trained deep learning models on CSAW-M to estimate the masking level, and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers — without being explicitly trained for these tasks — than its breast density counterparts.
Please find the paper corresponding to our work here and the GitHub repo here.CSAW-M Research Use LicensePlease read carefully all the terms and conditions of the CSAW-M Research Use License.
How to access the dataset:If you want to get access to the data, please use the "Request access to files" option above (currently, non-Swedish researchers need to have a general figshare account to be able to to request access). We will ask you to agree to our terms of conditions and provide us with some information about what you will use the data for. We will then receive the request and process it, after which you would be able to download all the files.If you use this Work, please cite our paper:@article{sorkhei2021csaw,
title={CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer},
author={Sorkhei, Moein and Liu, Yue and Azizpour, Hossein and Azavedo, Edward and Dembrower, Karin and Ntoula, Dimitra and Zouzos, Athanasios and Strand, Fredrik and Smith, Kevin},
year={2021}
}</div
Economics of Robust Surveillance on Exotic Animal Diseases: the Case of Bluetongue
No full textControl of emerging animal diseases critically depends on their early detection. However, designing surveillance programs for exotic and emerging diseases is very challenging because of knowledge gaps on the probability of incursion and mechanisms of spread. Using the example of Bluetongue Virus, which is exotic to the UK, we develop a metapopulation epidemic-economic modelling framework that considers the incursion, detection, spread and control of a disease in a livestock production system composed of heterogeneous subpopulations. The model is then embedded in an information gap (info-gap) framework to assess the robustness of surveillance and vaccination policies to unacceptable outbreaks losses and applied to the case of Bluetongue in the UK. The results show that active reporting of suspect clinical signs by farmers is a very robust way to reduce unacceptable outcomes. Vaccination of animals in high risk regions led to robustly protective programs. If vaccines are not available, surveillance targeted to the high risk region is very robust even if the extent of the high risk region is not known and effectiveness of detection is very low. Surveillance programs focusing in all regions with the same intensity are in general not robust unless the dispersal of the vector connecting both regions is very high.compartmental epidemic model, emergent animal disease, Knightian uncertainty, sentinel surveillance system, Livestock Production/Industries,
