BrianDehlinger
An aspiring software engineer with interest in Data Engineering, Software Architecture, and the Cloud
Open Commons ConsortiumChicago IL
Pinned Repositories
aergo
aergo blockchain kernel
Armadillo
A cloud-native app for nature lovers.
ArmadilloCord
A discord bot that allows users to play a multiplayer nature-themed adventure game.
BIOI500
BIOI 500 project - ARIBA, AMRFinderPlus, Resfinder
bpa-matrices
bpa-mtde.similarity
BloodPAC - Clustering Project Submission by MTDEs
DCB-Dynamic-Codon-Biaser
A tool for dynamically calculating the codon usage bias in bacterial genomes and querying of a database for Codon Bias statistical analysis
Hydrant
A C++ project for replicating data.
PrototypeDistributed
Stand-Arrow
The official repository for the Stand Arrow discord bot written in discord.py.
BrianDehlinger's Repositories
BrianDehlinger/DCB-Dynamic-Codon-Biaser
A tool for dynamically calculating the codon usage bias in bacterial genomes and querying of a database for Codon Bias statistical analysis
BrianDehlinger/Hydrant
A C++ project for replicating data.
BrianDehlinger/Stand-Arrow
The official repository for the Stand Arrow discord bot written in discord.py.
BrianDehlinger/aergo
aergo blockchain kernel
BrianDehlinger/Armadillo
A cloud-native app for nature lovers.
BrianDehlinger/ArmadilloCord
A discord bot that allows users to play a multiplayer nature-themed adventure game.
BrianDehlinger/BIOI500
BIOI 500 project - ARIBA, AMRFinderPlus, Resfinder
BrianDehlinger/bpadictionary
Data dictionary for the Blood PAC Commons
BrianDehlinger/PrototypeDistributed
BrianDehlinger/build-your-own-x
🤓 Build your own (insert technology here)
BrianDehlinger/cassandra
Mirror of Apache Cassandra
BrianDehlinger/cdis-manifest
Manifests tracking service versions to release to each environment
BrianDehlinger/cloud-automation
Automation for standing up Gen3 commons on AWS, GCP, Azure, and on-prem
BrianDehlinger/covidapp
Base project for COVIDstoplight and sibling apps
BrianDehlinger/CVE-2021-44228_scanner
Scanners for Jar files that may be vulnerable to CVE-2021-44228
BrianDehlinger/detect-secrets
An enterprise friendly way of detecting and preventing secrets in code.
BrianDehlinger/Distributed-Systems
Developing access for Front/Back Implementation Using Socket Communication
BrianDehlinger/Distributed-Systems-Mobile
Mobile Application for Distributed Systems Testing Application
BrianDehlinger/DistributedMobile
BrianDehlinger/dpytest
A package that assists in writing tests for discord.py
BrianDehlinger/Flare-Cogs
Assortment of cogs.
BrianDehlinger/gitops-qa
BrianDehlinger/HMEcoli
The repository for the tuxedo package analysis of HM27, HM46, HM65, and HM69 Ecoli strains
BrianDehlinger/indexd
Index service server
BrianDehlinger/InSilicoSeq
:rocket: A sequencing simulator
BrianDehlinger/KotlinBully
A Kotlin Implementation of the Bully Algorithm
BrianDehlinger/local-log4j-vuln-scanner
Simple local scanner for vulnerable log4j instances
BrianDehlinger/Shiftease
Automatic schedule creation and view
BrianDehlinger/SparkSentiment
Apache Spark and Sentiment Anaylsis.
BrianDehlinger/WASIS
WASIS (Wildlife Animal Sound Identification System) is a public-domain software that recognizes animal species based on their sounds. From a partnership between Laboratory of Information Systems (LIS) and Fonoteca Neotropical Jacques Vielliard (FNJV) of the Institute of Biology of the University of Campinas (UNICAMP), the main goal of this project is to design a tool which supports multiple algorithms to help scientists and general public on the identification of species. And why is it important? Besides the curiosity itself of knowing which species is calling, we can possibly identify invasive species in a certain area, help on establish migratory patterns from sounds of different locations during a specific period of time, as well as support long duration recording analysis. The software architecture was designed to support multiple audio feature techniques that extract meaningful information of animal sounds, and classification algorithms that use these extracted data to match against respective audio information stored in the software data repository. The main purpose of these algorithms is to design a classification scheme that can best predict the classes/labels for unseen data (an audio file that we want to identify), process similar to the human brain ability to differentiate among a wide range of sounds and to assign them to previously heard sounds.