Achieving Circular Wastewater Management with Machine Learning
Effective wastewater treatment is essential to the health of the environment and municipal wastewater treatment plants in Canada are required to achieve specific effluent water quality goals to minimize the impact of human-generated wastewater on the surrounding environment. Most wastewater treatment plants include a combination of physical, chemical, and biological unit processes and therefore have several energy inputs to drive mixing, maintain ideal temperatures, and move water from one unit process to the next. Methane and other gases (biogas) and biosolids are generated during wastewater treatment. Both of these can be captured and repurposed for use within and outside of the wastewater treatment plant and can in some cases even be converted to revenue streams. Thus, biogas and biosolids are considered recoverable resources rather than waste products. Circular wastewater management (CWM) is an emerging approach that aims to optimize wastewater treatment, energy usage, and resource recovery. To achieve CWM, the operators of wastewater treatment plants must have a thorough understanding and reliable control of the different elements of the system. This is usually achieved using a combination of operator expertise, online sensors, and offline water quality measurements coupled with data collection, storage, and analysis software. Conventional statistical approaches have traditionally been used to analyze the data generated in wastewater treatment plans, be these approaches are not flexible enough to fully describe and control the complex chemical and biological processes underlying wastewater treatment or to help utilities achieve CWM. Machine learning approaches are more flexible than conventional statistical approaches. In this project, we will use machine learning tools to identify opportunities to move towards circular wastewater management in a wastewater treatment plant operated by the Ontario Clean Water Agency.
Agile computing for rapid DNA sequencing in mobile platforms
DNA can now be measured in terms of electronic signals with pocket-sized semiconductor devices instead of 100-pound machines. This has started to transform DNA analysis into a mobile activity with the possibility to track and analyze the health of organisms at unprecedented levels of detail, time, and population. But the remote cloud-based computer services currently needed to process the electronic signals generated by these miniature DNA-meters cost over $100 to complete an initial analysis on one human genome and consume over 1000 Watts of power. Also, the cost of wirelessly transmitting measured data to these cloud-based analyzers can exceed $1000 per human genome. Further, reliance on external high-performance compute services poses a greater risk for compromising the security of the DNA data collected. This project proposes the construction of a specialized high-performance miniature computer – the agile base caller (ABC) – built from re-configurable silicon chips that can connect directly to the DNA-meter and analyze the DNA it measures in real-time.
The ABC, by virtue of its size, will preserve the mobility of emerging DNA measurement machines, and will enable them to analyze data for less than $1 while consuming less than 10 Watts. These cost/power performance improvements will significantly drop the barriers to the application of genomic analysis to non-laboratory settings. For example, they will allow continuous monitoring of Canada’s food supply for the presence of harmful biological agents with the possibility of cutting analysis delays from weeks to hours.
An economics-aware autonomic management system for big data applications
Recent advancements in software technology, including virtualization, microservices, and cloud computing, have created novel challenges and opportunities on developing and delivering software. Additionally, it has given rise to DevOps, a hybrid team responsible for both developing and managing the software system, and has led to the development of tools that take advantage of the enhanced flexibility and enable the automation of the software management cycle. In this new world characterized by volatility and speed, the Business Operations (BizOps) team is lagging behind and still remains disconnected from the DevOps team. BizOps views software as a product and is responsible for defining the business and economic strategy around it.
The goal of the proposed project is to imbue DevOps tools and processes with BizOps knowledge and metrics through formal models and methods. Currently, BizOps receives the software system or service as a finished product, a black box, on which a price has to be put and be offered to clients. The price and the marketing strategy are usually defined at the beginning of a sales cycle (e.g. a year) and remain the same for the entirety of the cycle. However, this is in contrast to the great volatility of the service itself. In most cases, the strategies are based on the instinct of managers with high acumen and experience and broad marketing surveys or one-to-one negotiations with clients, information that can easily change and may remain disconnected from the software development. The end product of this project is a set of economic and performance models to connect the DevOps and BizOps processes during the software’s life cycle and eventually incorporate them in automated tools to adapt and scale the system in production and enable continuous development, integration and delivery.
Characterization and Optimization of Heat Transfer from GRIP Metal Enhanced Surfaces
NUCAP specializes in brake pads and while improving their manufacturing process, they developed an innovative way to enhance the surface of metals in the form of small metal hooks (GRIP Metal). The raised features offer both increased surface and increased turbulence and flow mixing which can also greatly enhance convective heat transfer. The main objective of this project is to develop models which can accurately predict convective heat transfer and associated pressure drop for GRIP Metal enhanced surfaces. These will ultimately be used to optimize GRIP Metal surfaces for a wide range of heat exchange applications.
Computational support for big data analytics, information extraction and visualization
The Centre for Innovation in Visualization and Data Driven Design (CIVDDD), an Ontario ORF-RE project performs research for which SOSCIP resources are needed and they were awarded NSERC CRD funding with IBM Platform [Applications of IBM Platform Computing solutions for solving Data Analytics and 3D Scalable Video Cloud Transcoder Problems] beginning in July 2015. This project involves Big Data, Visualization and Transcoding and will train many HQP. We require access to equipment capable of running a multi-core cluster using IBM Symphony and Big Insights software with IBM Platform on data analytics, visualization and transcoding. Our objectives include:
IBM Platform:
- Test the applicability of Platform Symphony to Data Analytics problems to produce demonstrations of Symphony on application domains (we started by exploring streaming traffic analysis datasets) and identify improvements to Symphony to gain IBM advantage in the marketplace.
- Design and implement methods to greatly speed-up the search for high utility frequent itemsets in big data using Symphony in a parallel distributed environment.
- Design algorithms to determine which are suitable in such an environment.
- Identify commercialization venues in application domains.
- Exploration of a Scalable Video Cloud Transcoder for Wireless Multicasts
Continuous vital sign monitoring using intelligent bed sheet
Studio 1 labs developed wireless intelligent bed sheet patient monitoring system that continuously captures client vital signs. In collaboration with Dr. Laura Nicholson and York University’s Faculty of Health, Studio 1 labs will work with SOSCIP infrastructure to match vital signs with gold standards approved medical decides for the highest level of accuracy through clinical validation and scientific evidence. With millions of data points collected from each device to output clinical grade quality information continuously, AI solutions allow modeling to predict health emergencies and diseases. This contributes to efficient health monitoring solutions that are simple and effective for use by older adults and healthcare providers.
Development of severity index of exacerbation for COVID-19 symptoms from abnormal respiratory patterns
Struggling to breathe has been a challenge that existing monitoring devices have faced, for identifying early symptoms of respiratory diseases such as novel Coronavirus (COVID-19). Measuring respiratory rate alone would result in potentially missing if a patient was unable to inhale a full breath.
To overcome this challenge, a novel technology solution invented in Canada is used through an Intelligent Bed Sheet, with the ability to continuously monitor if a patient has normal, shallow, or irregular breathing.
This collaboration with York University will revolve around developing a Severity Index by identifying earlier when a patient is struggling to breathe. Using the most advanced electronic fabric technology, the SOSCIP Platform will support computing challenges by using large amounts of breathing data in a constantly changing environment, to precisely identify before a patient’s breathing becomes more severe.
Distributed Deep Learning and Graph Analytics Using IBM Spectrum Computing Solutions
Deep learning is a popular machine learning technique and has been applied to many real-world problems, ranging from computer vision to natural language processing. In most cases deep learning outperformed previous work. However, training a deep neural network is very time-consuming, especially on big data. A popular solution is to distribute and parallel the training process across multiple machines. Indeed, the race is on to parallelize deep learning! Industry and academic research teams around the world are trying to make deep neural networks train as fast as possible on farms of GPU capable servers. We are working with our IBM partners to help develop advanced scheduling and messaging techniques for distributed deep learning. In addition, we will develop two real-world applications of distributed deep learning to demonstrate the efficiency and effectiveness of distributed deep learning. In one application, we address the video surveillance problem of tracking a moving target over a network of video cameras with partial or no overlaps in their coverage. We will use a deep learning approach to identify multiple pedestrians in each video frame, and a particle filter to track moving pedestrians. In the second application, we address the problem of fraud/intrusion detection. We will use graph-based detection that considers relationships between objects or individuals. Graph-based approaches are powerful because they do not operate on objects or individuals in isolation, but also consider their network information. We will emphasize on graph-based fraud detection methods that have a number of applications and potentially large impacts.
Hybrid Graph Spectral / Data-driven Semi-Supervised Learning for Robust Classification
We study distributed classifier learning from a graph signal processing (GSP) perspective. In practical networked systems, big data often reside in geographically diverse regions scattered in local networks around the world. Leveraging on Cisco’s mature and extensive network infrastructure and our in-house expertise in GSP, we develop an efficient distributed graph-based classifier learning framework, by extracting and disseminating knowledge from data located across a large network. Specifically, we focus on two core problems in graph-based semi-supervised classifier learning: distributed graph sampling and distributed signal interpolation. Distributed graph sampling is the problem of pre-selecting nodes on the graph based solely on local network information to acquire informative labels. Distributed graph interpolation is the problem of interpolating missing labels in the remaining nodes, given a setoff observed labels in a local neighborhood. We approach both problems from a graph spectral perspective, resulting in a classifier system with theoretically derived and explainable performance guarantees.