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!
Our NSF project pitch was successful! After creating a multidisciplinary team with Atmospheric Sciences, Environmental Sciences, School of Natural Resources, Organicforecasting LLC., and other small businesses, we submitted a proposal to build a system for the nation to use related to predicting weather. The system would include weather data and provide a forecast between 15-35 days, 35-42 days, and yet another set that is vastly ignored from 42-84 days. Being able to accurately predict these forecasts has not been done before. We utilize a special blend of datasets, which are not being used elsewhere, and plan on expanding this methodology using machine learning to improve verification rates more.
The original system worked and the client was very happy. However, iOS came out with major changes in their OS and the original code was not working. We needed to re-develop the application using Swift, redo the entire user interface (UI), and then remake the Android version of the application to match the new iOS version. More functionality was requested and the user requirements changed. However, by using Agile development practices, we were able to learn and shift quickly to implement features liked by the client and remove features that were no longer needed.
After winning a pitch and securing a grant from the Entrepreneurship Alliance program, hosted by the Center for Entrepreneurship and Innovation (CEI) at the University of Missouri, we were tasked with developing an application that worked on all major platforms (e.g. Android, iOS, and web) that would search and scrap data from the web and popular social media platforms (e.g. Instagram) and display local specials and discounts at restaurants, bars, and various local businesses. Users could see these discounts and then use our app to find the business, basic business information such as hours, and display the coupon/deal at the business to get the discount.
The app and team was a success so further collaboration was created. The majority of the bicentennial website was developed with WordPress, however custom work needed to be done. After getting a great start on the mobile application, the State Historical Society of Missouri had us go in and create and update custom components of their website. Then we needed to integrate the web portion of the new events application which replaced their original calendar because it had limited functionality and WordPress was not able to perform the customizations they needed. This collaboration was so successful, that we were recommended to the Missouri State Fair administrators to use our event application and contract any additional work they may need.
A multidisciplinary team was created to develop a mobile application for the State of Missouri Historical Society. We used a MEAN stack and popular frameworks like Angular and Ionic to develop a cross-platform application. The app would allow the State Historical Society of Missouri to enter in all events, users could see those events in real-time, save events, add events to calendar, see others who plan on attending events, and can provide real-time feedback about the event. Essentially this application was a social media event application for all of the State of Missouri events and residents. After being successful, this collaboration opened up many doors working with various government agencies and private organizations throughout the midwest.
This collaboration grew larger and more successful. The Equine Hospital had more and more data, which became a massive amount of Big Data, and they needed more ways to search the data. As the data grew, efficiency was of more concern and key. At first, they would email us and we would run queries and return the results. We decided to spend a little extra time to create a dashboard, where users could specify all the requirements they wanted, just like they did in the emails, and run the results themselves, which in turn saved us a lot of time. We created a centralized dashboard for users to view and download data efficiently across multiple cloud instances. The data was stored securely and dashboard access was only given to authenticated users. The dashboard was so dynamic and successful, which the client was not expecting us to create, more projects were in discussion.
Word quickly spread about the successful projects we designed and developed. Many projects were being presented to Streamline. The Industrial and Manufacturing Systems Engineering (IMSE) chair reached out and wanted a website developed that would keep track of all IMSE alumni as a private social media site. A website and non-relational database were created. In addition, a Python web scraper was built to pull information, such as LinkedIn profile information, and store that information to the secure database, so people can stay up-to-date with other colleagues, all happening in real-time automatically. IMSE staff and faculty would upload a list of currently graduating students, the scraper would run and pull corresponding information at scheduled intervals, alumni could submit an application to be considered for the hall of fame on the website, and once accepted, hall of fame alumni would be given special privileges. More functionality for the social media site is in discussion and will be implemented in phase 2.
This collaboration was very successful and more tasks were given. The Director of E. Paige Laurie Endowed Equine Lameness Program reached out and needed a unique item created that could only be completed using custom 3D printing to save multiple horses lives. Some Missouri local Amish had horses that could not breath due to a common illness horses get when eating non-commercialized food. The Amish also did not have much funding to take the horses to get surgery which costs several thousand dollars. To save the horses lives, they stuck a garden hose down the horses throat so it could breath. The hose was soft and flexible but it could also be kinked which could result in the horse not getting sufficient oxygen. We heard the problem and helped design a trachea insert that would be inserted through the larynx. 3D printing is something we have never done before, but using Agile development just like in software, we were able to create the device and save horses lives. We plan to try to patent this device and see if it could save other horses lives as well, since this disease is normal in horses that eat natural food from the ground, such as farmland grass.
The occurrence of severe weather is an annual problem for much of the United States and North America from March through July. With the increased interest in sub-seasonal weather forecasting, there have been attempts to anticipate the occurrence of anomalous weather on the time scale of one to four weeks including the occurrence of severe weather. Previous research has shown that teleconnection indexes, associated with long period Rossby Wave activity, or persistent large-scale flow regimes have been useful tools in this endeavor. Here, abrupt changes over a 24 – 72 hour period in the Southern Oscillation Index (SOI) time series will be used to demonstrate that these changes can be associated with the possible occurrence of major severe weather event-days (e.g. 20 or more tornadoes, 155 or more wind events > 25.9 meters per second, or 135 hail events larger than 25.4 mm) over the US one to three weeks in advance, especially during the severe weather season. The severe weather events obtained from the archive at the Storm Prediction Center (SPC) from 1991 through 2020 were used. The results here demonstrate that more than seven in ten major severe weather occurrences were associated with abrupt changes in the SOI when using a strict test of the predictability.
The original system was built with traditional machine learning algorithms as well as standard statistical models. Many of the original models were developed using Matlab. The prediction results were decent but more improvement could be made. We designed and developed a new machine learning infrastructure using Python that could use various machine learning and deep learning models. Some of the deep learning models did not produce significantly better results. However, we implemented an algorithm that could produce more features. After having more human and computer generate features, we were able to achieve better accuracy and less false positives than previous implementations. False positives are important for this project because if a horse is predicted as positive, they need to go through an intensive procedure to determine if they have spinal fluid consistent with ataxia, which is very invasive for the horse and very expensive. Therefore, the lower the false positives, the happier the horses and our client, even if that meant slightly lower accuracy (i.e. Precision and Recall).
A 3-year proposal was submitted to the NSF for 1.2 million in order for us to design and build a multidisciplinary team of Soil Microbial Ecologists, Soil Health Experts, Soil and Environmental Sciences, Plant Science & Technology, IT Professionals, Computer Scientists, Machine Learning Engineers, and Software Engineers. This team would collaborate to build a real-time intensive cyber-infrastructure support system with data fusion from soil-based sensors with remote sensing data to provide high-resolution spatial coverage of soil properties. This real-time cyber system will be used for secure collection, storage, and sharing of massive amounts of data by many wireless sensors. A dashboard was created to innovative data analytics and visualization (i.e. Geovis) tools to enable quantitative data from soil sensors to be represented and interpreted over space and time. A phase 2 application will be submitted in order to analyze the data and use machine learning and deep learning to predict soil quality and best areas for growth on specific plant species.
The required Project Pitch allows startups and small businesses to get quick feedback at the start of their application for Phase I funding from America’s Seed Fund powered by NSF. Startups or entrepreneurs who submit a Project Pitch will find out if they meet the program’s objectives to support innovative technologies that show promise of commercial and/or societal impact and involve a level of technical risk. They will also get additional guidance and feedback from NSF staff. If your Project Pitch is a good fit for the program, you will receive an official invitation from NSF to submit a full proposal. If you’re not invited to submit, you’ll be told why your project is not appropriate for the program. We formed a multidisciplinary group with Atmospheric Sciences, Environmental Sciences, School of Natural Resources, Organicforecasting LLC., and other small businesses to develop a system for the nation to use related to predicting weather. We utilize a special blend of datasets, which are not being used elsewhere, and plan on expanding this methodology using machine learning to improve verification rates more.
Many projects were being brought to Streamline after all the good reviews from all the good work we had completed. The Director of the Equine Endowed Program at the University of Missouri reached out to us and needed work completed. They had a 20+ year dataset that needed analyzing for patterns. The final result was to use the data collected from sensors to predict ataxia and lameness in horses. They would attach sensors to each leg, the head, and the pelvis of the horse. The horse would perform tasks, and we would use that data to determine if the horse was ataxic or lame. After many rounds of collaboration with many multidisciplinary team members, we were able to reach high accuracy (i.e. precision and recall), and we were able to have a low false positive rate. False positives are important for this project because if a horse is predicted as positive, they need to go through an intensive procedure to determine if they have ataxia or lameness, which is very invasive for the horse and very expensive. Since we were working with the University, the research was published in scholarly articles and many students used this work as their graduate thesis.
The original system worked and no major bugs. This system increased the efficiency of many departments and is the main system being used today. After the first version of the system was successful, new features needed to be added, and previous features needed updates. With new features being added, this system can be used by more departments, staff, and faculty members. If the next version is successful, I can see this system being used across the entire University of Missouri system to help efficiency, accuracy, and make everyone’s life easier.
Mizzou was sending too many emails to students and was flooding their inboxes with updates about events, seminars, news, course updates, etc. Students were overwhelmed and were not getting important emails about special events like large companies coming to campus (i.e. Google, Microsoft, etc.). Students were reporting not checking their emails because of the mass amount of emails they received each day. The college of engineering wanted an application where they can post events to students, faculty, and the public. This project will have several stages, the first stage will consist of having events show up to the correct people with push notifications, getting feedback to know who plans to come to the event, allowing the user to give feedback about the event, having the user share the event with their friends, and much more for future stages.
The University of Pittsburgh wanted to add on to their existing system. They already had a data collection application but wanted to get a valid BAC reading from a breathalyzer device using Bluetooth connecting to mobile device. We developed an API and connection to bluetooth device and transferred the information to their already built system, using secure and HIPPA compliant protocols.
Working with the Digital Biology Lab and Biochemistry, we wanted to create a one-stop shop for everything dealing with seed oil. Increasing seed oil content by plant breeding has resulted in trade-offs or penalties with respect to protein content, seed size, or seed set. The molecular basis for this impasse is mostly speculative. Use of current global profiling approaches to better understand both the metabolic consequences of higher oil and the basis for reduced yield must also deal with off-target genetic mutations (even in near-isogenic lines), ultimately confounding cause-effect interpretations. We propose a diverse, integrated strategy to study the consequences of higher lipid production by studying transgenic plants specifically engineered to produce higher seed oil.
What type of engineer are you? Mizzou wanted to make a fun quiz for future or current students to take in order to “predict” what type of engineer they could be. The survey is not a concrete algorithmic prediction, but more of a fun way to get students more involved in engineering. Some of the questions came from known research and others were just for fun. At the end, the quiz will show all the majors at Mizzou and have a percentage score for each major and which one would be good for you. After taking the quiz, you can click on the majors and link back to the Mizzou college of engineering website for more information on that major and degree.
A research community to collaborate on research projects and a tool for students and other public users to find various research projects and activities throughout the world. A collaboration network is generated as users add accounts. Each user has a private page and a public page. The private page is only available to users in the Yaffle system. The public page is viewable by the public and used as advertising for that user to connect with people around the world. A heat map of the research projects around the world is generated for global connections.
A workflow system to replace paper-based submissions for various requests through the college of engineering (COE) at the University of Missouri. The current system at the college has several requests based on Fiscal, Human Resources, Marketing and Communication, etc. There may be a request to reserve a room, reserve a vehicle, hire or terminate faculty, payroll, website revisions, and many more various requests. In the past, these requests were manual, labor intensive, and error prone. With the new COE Workflow System, the entire workflow can be made online, which is very efficient, accurate, and allows staff to get more request done. This system has saved Mizzou valuable time and effort which can then be used for other tasks.
A dashboard created to help Mizzou manage their massive amounts of research data in real-time while allowing administrators to focus on analyzing the data rather than trying to find and manage the data. Mizzou has large amounts of research data including research expenditures, research proposals, grants received, grants submitted, who is working on which project, and the list goes on. This data is classified by different metrics like by shared credit, managing unit, etc. The dashboard organizes the data and displays results visually. Data breakdowns by university, college, department, and individual investigator are reported. This will help promote collaboration, minimize error, and recognize faculty members for helping grow innovative research at Mizzou's four universities.
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.
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.
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.
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.