Developing and implementing high quality custom software applications. Anything from mobile applications on iOS and android to desktop and web applications.
Our services bring data analytics, natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, speech-to-text, and machine learning (ML) technologies to life.
We can increase the security of already implemented systems or add our own level of protection.
Developing Mobile Applications is exactly what we can do. We develop iOS, Android, and Hybrid applications. We have the experience to back us up too!
Do you have lots of data? We can clean, process, and analyze or predict various information from the data. We have been working on Big Data Analytics for the Missouri Department of Conservation for over 3+ years!
A real-time web application was created for TigerAware to help researchers build surveys and control who can participate in a survey or deploy to a public audience, with additional ability to set scheduled and random notifications. The dashboard also provides researcher capabilities to structure, organize, analyze and visualize their results. TigerAware Dashboard is a full stack application consisting of a combination of RESTful Web services, an OAuth 2.0 endpoint and other background services hosted on Amazon EC-2 and consumed by an AngularJS front end framework, which uses modular design methodology along with various open source libraries like Highcharts.js to display rich and interactive graphs.
In this project, several state-of-the-art deep learning models and their combinations have been applied to fish recognition in images, in particular 9 common species of fish in Missouri rivers. Four different data processsing and maching learning pipelines have been developed and extensive experiments have been conducted to evaluate their performances to form a working product for the Missouri Department of Conservation.
Turning Ecological Momentary Assessment (EMA) for a particular psychology group into an iOS mobile application. The application is a dynamic data collection tool that is able to change questions on the fly without recompiling the application. A dashboard is included to make the questions and send them to the mobile app. Technologies such as Firebase, iOS, ResearchKit, and Bluetooth Breathalyzers were used.
In this project, an integrated mobile data collection and web-based data management system, called Creel Survey Data Management Application (CSDMA), has been successfully designed and developed for the Missouri Department of Conservation. CSDMA is comprised of two major components: 1) the Creel Survey Mobile Application – a Hybrid application developed using Phonegap and Apache Cordova, which collect survey data on an iPad or an iPhone, and uploads them to a MySQL database on an Amazon Web Services (AWS) EC2 instance, and 2) the Creel Survey Web Dashboard developed using LAMP stack, which organizes and displays the aforementioned data for biologists and conservation staffs to view and verify in real-time using a user-friendly web interface developed using the Bootstrap CSS3 framework.
The Business School at the University of Missouri was impressed with what we could do, which brought them back for more. We decided to focus more on the transcripts and audio data combined, we generated our own features as well as state-of-the-art features, and developed a new system. This work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment and is negative otherwise. In this project, we developed a data processing and maching learning pipeline.
For this project, audio sentiment analysis was applied to call center conversations between a salesperson and a customer to predict if the customer would set up a sales meeting based upon their sentiment during the call. The two systems developed from this project are an end-to-end sentiment analysis pipeline for segmenting, performing feature extraction, and classifying conversational audio files using classical machine learning methods and a second end-to-end sentiment analysis pipeline that utilizes a deep Recurrent Neural Network to predict sentiment.
A Data Analytical Web Application for Psychological Research Studies. Data drives decision making. A web application dashboard helps in visualizing data intensive application logic and allows quick access to various business metrics. It also provides ease of access to data at one place. A configurable dashboard can allow various user actions and easy maintainability. In this project, a Web Application is developed in collaboration with MU Department of Psychology to handle large sets of research survey and sensor data.
Measure Square, Inc. in Pasadena, CA had a massive dataset of floor plans for buildings, houses, and various structures. They were calculating the results by hand and wanted a way to automatically calculate different features using image processing techniques. They came to us and we decided we could develop the algorithms. We started out implementing image processing techniques but quickly went to machine learning. There we develop various algorithms and generated the results they were after.
We have been working on various research projects for the Missouri Department of Conservation, Psychology Department at the University of Missouri, Marking and Business Schools, and many others. We then decided to branch out and expand our marketable skillset. Now we are Streamline Technology Company and we are ready to work for you!
The Missouri Department of Conservation (MDC) has massive amounts of imagery data for birds around Missouri. With that data, they try to keep track of wildlife and vegetation around the state. One large bird population that needs to be accounted for are Waterfowl. From the image, MDC would manually count the number of birds by hand. Instead, they reached out to us in order to develop a machine learning algorithm that would count the number of birds automatically. This application is powerful and has great potential for future work in different areas.
We developed machine and deep learning algorithms that would take the transcripts and recorded audio from a cold sales call and perform sentiment and audio analysis. This project was developed for the Business School at the University of Missouri and the collaboration is still strong today.
This project provided a machine learning pipeline that would take psychophysiological data and classify mod dysregulation using various algorithms. The best results obtained were approximately 90% accuracy when adding categories for the time of day. This project was used by the Psychology Department at the University of Missouri for one of their large grant proposals.
We provide a comprehensive platform, MUFOLD, for efficient and consistently accurate protein tertiary structure prediction. The long-term objective of MUFOLD is to help experimental biologists understand structures and functions of the proteins of their interest thereby facilitating hypotheses for experimental design. Currently, in MUFOLD platform we already provided pdbLight, a web-based database which integrates protein sequence and structure data from multiple sources for protein structure prediction and analysis, MUFOLD 3D structure prediction, a web-server which provides the structure predictions from the sequences that users submitted, and MUFOLD-CL, a fast tool for protein structural model clustering, visualization and quality assessment.
Our previous hybrid app was very successful and MDC liked the idea of having the application on any mobile operating system. Therefore, they wanted a new application for creel
After we had built several systems for our Psychology Collaborators, we became smarter. Instead of building multiple systems, we determined we would build one system that could dynamically change based off the Psychology needs. This was the start of MoodToolkit, a system that did just that. MoodToolkit was an iOS based application where the interface was built on a web-based dashboard. After building the layouts, the administrator would update the application on all the user's devices by launching the study. Now, any data the Psychology wanted to collect they would be able to because they had the capability to create their own surveys in real-time. We collect this data in a NoSQL database and would be stored for later analysis.
We were collecting data for the Psychology projects for approximately a year and the system was solid. Now our Psychology collaborators wanted to use this data to predict whether the person was drinking alcohol or not. The system was continuously collect physiological data like heart rate, breathing rate, ECG, activity, and many more. We would use this data and analyze it in real-time to predict whether the user started to drink alcohol using machine and deep learning techniques. If we determined the usesr was drinking, we would prompt the user to complete a survey which would then allow us to better understand what causes people to drink.
The Missouri Department of Conservation (MDC) agents need to keep track whether people fishing are catching the proper size fish that meets regulations. Normally the agent would need to manually measure the fish with some tools. This method is easy to have errors and requires manual labor. MDC wanted a way where agents could take a picture of the fish with their mobile device, the picture could contain one fish or many fish, and the system would report back the size using image processing and machine learning. Later, we also want to classify which type of fish, for example, the user would take a picture of a fish, the system would analyze the fish, determine the size, and report back what type of fish it is, all happening in real-time.
Streamline Technology and the Missouri Department of Conservation's (MDC) relationship continued to build. In Missouri, Chronic Wasting Disease (CWD) is a serious problem and has a possibility of harming humans. MDC wanted an application where users could input their location, or have it automatically collected, input how many deer they saw with this illness, and have the data collected for processing. This would help Missouri keep track of this disease and help manage it to protect humans.
The Missouri Department of Conservation (MDC) continued to be more than happy with our work so they continued to transition into the digital world using technology from Streamline Technology systems. MDC put GPS collars on bear and deer around Missouri and wanted to use this data to analyze their patterns. For example, they wanted to know which bears interacted with each other, where the bear like to eat, and during what times of the year where they would migrate. The GPS data was collected at certain intervals and we use the data to produce the results they needed to find.
The Missouri Department of Conservation wanted to release an application to the public and they determined most users do not have Android based systems and they would need an application that would work on all mobile operating systems. Therefore, we developed this as a Hybrid Mobile Application where the app could be installed on any operating system. This app was used to collect user observations of animals they saw such as deer. The app would collect the user's location and how many animals they saw, almost like a hunting log. The log could be shared amoung users or kept privately. This app would help the conservation department to keep track of wildlife populations and help users determine the best spot to hunt.
The Missouri Department of Conservation was very pleased with our first few systems and most of the data collected by them was on pen and paper. Instead of making a new app each time, which might take a few weeks to a few months, they wanted an application that could be dynamic so they could have one app for all of their surveys collection needs. We built an Android based system where the app's interface would dynamically changed based on the data in the database.
The good word continued to spread and another psychology research lab receved a grant from NIMH and needed an application to collect user input, brain scans, and physiological data collected from Hexoskin. We developed this system and later we would analyze the data for them to predict stress and mood dysregulation in real-time.
In the psychology world, the good word got around about our great system for the University of Missouri Psychology Department. St. Louis University wanted a similar system for an EMA study they wanted to perform. We developed this app within a few months and deployed it with good results.
The Missouri Department of Conservation was so impressed with our system because it was configured and customized to exactly what they wanted, we began working on the black bear data visualization and analysis project in parallel with the Shooting Range System.
We begin working on the shooting range system and the Mizzou Alcohol Craving Study for the University of Missouri approximately at the same time. This system would collect data from the user and try to predict when that person was using alcohol. The Psychology Department was so excited with what we could provide, they asked for several additional products.
We begin working on the shooting range data collection and visualization system. We develped an Android based mobile appication with an HTML5 web-based visualation tool. MDC was very satisfied and wanted other custom technology tools.
In the summer of 2014, the Missouri Department of Conservation needed a data collection tool for various shooting ranges across the state of Missouri. In addition, the Mizzou Psychology Department needed a survey based tool to collect information about why people use alcohol. This started everything.