Chicago, IL, United States
Chicago, IL, United States

Illinois Institute of Technology, commonly called Illinois Tech or IIT, is a private Ph.D.-granting research university located in Chicago, in the U.S. state of Illinois, with programs in engineering, science, psychology, architecture, business, communications, industrial technology, information technology, design and law. Wikipedia.


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Patent
Illinois Institute of Technology | Date: 2016-07-22

A region of interest (ROI) generation method for stereo-based pedestrian detection systems. A vertical gradient of a clustered depth map is used to find ground plane and variable-sized bounding boxes are extracted on a boundary of the ground plane as ROIs. The ROIs are then classified into pedestrian and non-pedestrian classes. Simulation results show the algorithm outperforms the existing monocular and stereo-based methods.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 500.00K | Year: 2016

Wireless communications powered by renewable energy sources have been emerging as a promising solution to mitigate the carbon footprints to achieve a green radio network. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity, which poses new challenges in the design and deployment of a sustainable communication network. The project will develop theoretical tools, propose new protocols and cross-layer optimization methods to enable an energy sustainable communication network. The multidisciplinary project will also foster the integration of research and education and provide both undergraduate and graduate students, particularly women and minority students, with an opportunity to participate in various training projects, which further inspire students to pursue high quality research with critical thinking.

In a renewable energy powered communication network, the fundamental design criterion and main performance metric have shifted from energy efficiency to energy sustainability, i.e., to ensure the harvested energy can sustain the user demands with satisfactory quality of service provisioning. In this project, a novel performance metric, the energy sustainability, is first formulated, based on which the energy sustainable performance of a communication system will be systematically analyzed, characterizing the dynamic energy charging and discharging processes. The analysis will be leveraged to investigate a series of fundamental research issues on sustainable communication and networking, including energy management, network deployment, admission control, adaptive resource allocation, and medium access control. Analytical tools in queueing theory, game theory, stochastic optimization, probability theory, and random processes will be used in the design, analysis, and optimization of the proposed algorithms and protocols to ensure energy sustainable operation of a wireless communication network. The research outcomes of the project have potential to be implemented in the next generation wireless communication network powered by renewable energy.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 500.00K | Year: 2016

Traffic congestion jeopardizes the function of urban transportation systems and has a growing negative effect on the health of urban economies. It also increases air pollution with numerous negative health impacts on our citizenry. A promising solution to alleviating traffic congestion is to establish coordinated driving mechanisms. This is enabled by recent connected or even autonomous vehicle technologies and advanced onboard computing facilities. However, engineers who design such mechanisms are still lacking scientific knowledge and effective tools that can be proven as efficient and reliable for use by the general public. The goal of this Faculty Early Career Development (CAREER) program award is to develop innovative approaches to the coordination of connected vehicle drivers- online route choices. This will be done by exploiting emerging information and computing technologies equipped in connected transportation infrastructure. This approache will improve transportation system mobility, safety, and environmental sustainability without sacrificing the interests of the individual vehicles. This research will deepen our understanding of the competition among vehicles on limited traffic resources. It should also reveal the impacts of the decisions of individual vehicles on traffic congestion, and offer a new paradigm of real-time traffic control.

The specific research objectives of this CAREER project are: (a) developing coordinated mechanisms for drivers - route choices to mitigate over-competition; (b) engineering collective effects of drivers - decisions to improve system-level performance; and (c) implementing the coordinated routing mechanisms and decentralized traffic control in an online environment. If successful, this project will lead to: (1) game-theory based modeling, analysis, and design techniques for coordinated routing mechanisms; (2) innovative methods for integrating decentralized control into individual routing decisions via intentional information perturbation techniques; and (3) better understanding of convergence, efficiency and robustness of distributed algorithms for decentralized congestion control. The reseach approaches include: (a) using game theory, optimization, and traffic dynamics to design and analyze coordination mechanisms; (b) using equilibrium analysis, price of anarchy, bounded rationality, and decentralized control for the study of collective effects; and (c) developing distributed algorithms for implementation and carrying out a theoretical analysis of these algorithms. This project will involve underrepresented K-12 students, undergraduate and graduate college students in numerous research tasks, and disseminate research findings through multiple channels, such as national/international workshops and conferences, as well as journal publications.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: INFO INTEGRATION & INFORMATICS | Award Amount: 471.99K | Year: 2016

Measuring public perceptions and how they change over time is a central problem in marketing, public health, and politics. Traditional measurement methods rely on surveys and focus groups, which can be costly and time-consuming. Online social networks offer an attractive alternative: real-time perceptions can be estimated from public, online activity and compared with an entitys communications to quantify how public messaging affects perception. While prior algorithmic approaches rely purely on text-based sentiment analysis, this project will develop novel methods based on the insight that an entitys online social connections are indicative of how they are perceived (e.g., birds of a feather flock together). Thus, rather than typical one-dimensional measures of sentiment, the project will instead investigate public perception with respect to multiple characteristics of an entity (e.g., is it seen as pro-environment, pro-health, etc.). A multi-faceted evaluation will be performed to study the phenomenon of greenwashing, a deceptive marketing practice in which firms market their products or policies as more environmentally friendly than they truly are. This project has the potential to enhance consumer protection by exposing deceptive marketing practices.

The project will develop social network analysis algorithms to assess perception of an entity and also language processing algorithms to quantify the communications of an entity with respect to a perceptual attribute. The approaches to both problems rely on innovative algorithms to measure the strengths of the social and linguistic relations between public entities and exemplar accounts that typify the perceptual attribute of interest. A key advantage of the approach is its minimal requirement of human input, e.g., given only a single keyword like environment, the approach identifies suitable exemplars and fits linguistic and perceptual models. The project will develop novel machine learning methods for domain adaptation, positive-unlabeled learning, and learning from label proportions in order to fit such models and ensure they are robust to omitted variable bias. The models will be evaluated using public Twitter and Facebook data to quantify the relationship between the perceptions and online communications of brands and other public entities, with a particular focus on identifying cases of greenwashing.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Campus Cyberinfrastrc (CC-NIE) | Award Amount: 342.80K | Year: 2017

To drive innovation, improve research capabilities and productivity, enhance faculty competiveness, and foster remote collaborations of resources and people, the Illinois Institute of Technology (IIT or Illinois Tech) is building a Science DMZ (Demilitarized Zone) ecosystem to provide access to a secured, high-throughput infrastructure for the IIT research community. The proposed Science DMZ bridges the needs of many diverse data-intensive research projects and address common issues and limitations with real-time data analysis that results from the current campus network. The overall goal for the Science DMZ is to deploy a scalable research network infrastructure with a minimum bandwidth of 10Gbps that can isolate and secure research network traffic from other segments of the campus network without impacting performance. This goal is buttressed by several broad objectives including increasing the bandwidth between the research data center and Internet2 exchange node; and connecting with other world-class universities and research center at throughput speeds sufficient for practical end-to-end research collaborations.

The deployment of the Science DMZ has far-reaching broader impacts. For example, the Science DMZ makes it feasible for IIT researchers analyzing urban metropolitan transportation congestion relief, safety and capital investment to improve the efficiency of intersection utilization across the United States. The Institute of Food Safety and Health (IFSH) is working on a whole genome sequencing project that aims to create a global catalogue of bacteria-causing food poisoning. This advanced high performance infrastructure provides greater collaboration opportunities with Tier-1 research universities and by default, introduces greater opportunities for IIT students to get hands-on experience working with faculty on various research projects, thereby improving the quality of their STEM education. Collaborative activities are also planned with several of IITs K-12 outreach initiatives including the Global Leaders Program, which seeks to increase access to STEM fields.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: COMMS, CIRCUITS & SENS SYS | Award Amount: 380.00K | Year: 2016

The rapid development and wide deployment of wireless networks incur a fast escalation of energy demand, which eagerly calls for energy-efficient networking techniques. At the same time, wireless networks are evolving into complex forms with multi-dimensional resources including communication link, radio, channel, antenna, and transmit power; algorithms with low complexity for energy efficiency optimization are highly demanded. This project targets at a fundamental study on energy-efficient wireless networking through establishment of a uniformed analytical framework, development of efficient and low-complexity algorithms, and application of the generic studies into important scenarios in the fifth generation (5G) cellular systems. This interdisciplinary research will not only provide various training projects to undergraduate and graduate studies, but also inspire students to pursue high-quality research with a creative, open-minded, and cross-disciplinary perspective.

This project is going to demonstrate that a uniformed multidimensional optimization framework for energy efficiency optimization can be constructed by the principle of scheduling proper transmission patterns, which are defined by the interference model of the network. With such a uniformed optimization model, low-complexity decomposition techniques are fundamentally related to a maximum weighted transmission pattern (MWTP) problem, under a physical interference model according to the signal-to-interference-plus-noise ratio. Approximation algorithms and associated performance analysis for the MWTP problem (which is NP-hard in general) are critical research issues to be studied. Distributed algorithms for solving the energy efficiency optimization problem further involves decomposition with multi-objective optimization, and innovative Lyapunov function design and associated stability analysis under the physical interference model, which will also be addressed in this project. Energy efficient solutions in a couple of important 5G cellular scenarios will be enabled through innovative modeling and algorithms in the uniformed multidimensional framework, including joint optimization that incorporates massive MIMO interference mitigation with flow constraint at network layer and base station sleeping at system level, formulation and algorithm development for a MWTP problem under the massive MIMO interference model, and modeling of the interplay between massive MIMO and device-to-device communications. In this project, the proposed research seamlessly integrates studies in the areas of optimization, graph theory, dual decomposition, approximation algorithms, and wireless communication and networking. The research outcomes are expected to provide important guidance for the development of the 5G cellular systems.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: CAREER: FACULTY EARLY CAR DEV | Award Amount: 500.00K | Year: 2016

This Faculty Early Career Development (CAREER) project supports fundamental research needed to realize mechanisms of control of waves in solids. Information and energy in the world travel from one point to another in the form of waves. Examples include electromagnetic waves such as light and radio waves, sound waves in air and water, and elastic waves in solids. The ability to control the flow of these waves, therefore, indirectly leads to the ability to control the information and energy which these waves represent. Strong material design mechanisms have recently been developed to control the flow of electromagnetic and acoustic waves. However, controlling waves in solids has proven to be more difficult, and resolving associated challenges is the main focus of this project. This research will have beneficial impact on several U.S. economic, security, and energy interests. It will lead to improvements in the design of vibration sensors, transducers, and imaging devices with applications to various industries such as aerospace, automobile, civil infrastructure. It will lead to novel earthquake mitigation techniques for civil structures and vibration mitigation techniques for sensitive industry equipment. Through the various outreach efforts proposed here, this research will help in broadening the participation of the general public in the highly multi-disciplinary subject of waves and their control.

Transformation methods have emerged as effective tools for the control of electromagnetic and acoustic waves. However, analogous successes have not been achieved for elastodynamics (waves in solids). This research aims to fill the knowledge gap by investigating a coupled constitutive form (Willis form) as the basis upon which the principles and applications of transformation elastodynamics can be built. Furthermore, this research aims to provide fundamental limits and bounds on the performance of any transformation device through the application of causality principle and scattering theory. The PI will conduct theoretical studies into elastodynamic homogenization, performance bounds of transformation-elastodynamic devices and will assess performance gains through the application of classes of Euclidean and non-Euclidean transformations. Level-set based parallel computational algorithms will be also developed for inverse design of transformation-based devices. Three-dimensional printed models of transformation-elastodynamic devices, at both the unit cell and the device levels, will be fabricated and tested through ultrasonic wave measurements for experimental verification.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: BIOPHOTONICS, IMAGING &SENSING | Award Amount: 500.00K | Year: 2017

ABSTRACT: Tichauer; 1653627

Approximately 1 in 8 women in the US will develop breast cancer in their lifetime. Over 40,000 women are projected to die from the disease in 2016. Surgical resection and pathology of tumor-draining lymph nodes is the current standard for detecting whether cancer has spread. However, because of the time-consuming nature of pathological assessment, less than 1% of a typical excised node is surveyed and microscopic levels of cancer are liable to be missed. The objective of this work is to rapidly map cancer distribution in surgically excised tumor-draining or sentinel lymph nodes using a novel optical imaging technology that could guide sectioning of lymph nodes in pathology so that even microscopic levels of cancer are not missed.

The approach, named ADEPT (Agent Dependent Early Photon Tomography), combines two methods that serve to provide high sensitivity and selectivity for cancer cells within highly scattering media. First, an infrared-emitting dye perfuse the node, with one dye selectively targeted to cancer cells, while the other is nonspecific, enabling compensation for local variability in dye concentration. Second, pulse excitation combined with snapshot detection of the earliest photons-to-arrive-at-the detector are counted. To obtain 3-D reconstructions of the internal lymph node volume, the image plane is scanned through the volume. With the appropriate selection of dyes, the approach can be applied to thick tissue (up to 1 cm), with a goal of 100 um image resolution.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: National Robotics Initiative | Award Amount: 899.93K | Year: 2016

The objective of this research is to ensure the integrity of vehicle position, heading, and velocity estimates that are used by self-driving cars as the basis for life-critical decisions such as the initiation and execution of hazard-avoidance maneuvers. Integrity, which is a measure of trust in a sensors information, has been successfully implemented in commercial aircraft to guarantee the safety of maneuvers such as landing. This project addresses several obstacles in translating integrity from aviation applications to self-driving cars, including integrating the disparate sensor types used by ground vehicles; meeting the stringent demands of routine autonomous driving; accounting for the number, proximity, and high relative velocity of other vehicles on the road; and evaluating multiple, distinct, and mutually exclusive courses of action in a timely manner. Project subtasks include characterization of integrity for representative sensors, construction of appropriate models for uncertainty propagation, and experimental validation of the resulting integrity framework. The project will advance the larger research effort to realize the potential of self-driving cars for relieving congestion, reducing emissions, and saving lives. The work includes public outreach efforts on autonomous navigation for self-driving cars, which will build upon an ongoing relationship with Chicagos Museum of Science and Industry, including a hands-on demonstration during National Robotics Week to illustrate how safety can be ensured despite uncertainties related to sensor readings, vehicle dynamics, and the driving environment.

Specifically, this research will provide new experimental and analytical methods to quantify and prove self-driving car safety. The results of this work will create a high-level, sensor-independent, quantifiable metric that can be used to compare, evaluate, and certify safety across self-driving car manufacturers. Knowledge will be advanced in several previously-unexplored areas, including first-ever demonstrations of: 1) high-integrity sensor measurement error and fault models for non-GPS sensors, 2) analytical methods to quantify the safety risk of feature extraction and data association algorithms required in lidar, radar, and camera-based localization, 3) multi-sensor pose estimators and integrity monitors designed to evaluate the impact of undetected sensor faults on safety risk, and 4) rigorously derived and experimentally validated integrity risk prediction methods in dynamic environments.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: CAREER: FACULTY EARLY CAR DEV | Award Amount: 500.00K | Year: 2016

This Faculty Early Career Development (CAREER) project will investigate the dynamics and control of advanced combustion strategies that have the potential to increase the efficiency of fuel-flexible diesel engines by up to 20 percent. The use of alternative fuels in modern vehicles typically results in higher production of some pollutants, as well as a drop in efficiency. However, the combination of alternative fuels along with more advanced combustion techniques has the potential to solve this problem, and provide efficient, clean power for transportation. While the benefits of this strategy have been demonstrated in highly monitored laboratory environments, significant improvements in the control of multi-cylinder engines are needed before these benefits can be realized in production vehicles. This project will create estimation and control methods for complex engine systems. The project will also provide opportunities for underrepresented students to work in this critical area of transportation energy research.

The control of advanced combustion engines is particularly challenging due to the low availability of sensor measurements in the harsh engine environment, the highly nonlinear and internally coupled behavior of the system, and significant cycle-to-cycle and cylinder-to-cylinder variations. To meet these challenges, this research will study and model the dynamics of a multi-cylinder advanced engine system. With an improved understanding of the dynamics, the research team will then create nonlinear estimation techniques that capture key variables for which measurements are not available and which are primary drivers in combustion variations. This project will culminate in the investigation of nonlinear model predictive control techniques that can provide optimal performance to this complex system with its constrained and coupled inputs. While reliable linear control techniques have existed for decades, control strategies for highly nonlinear applications such as advanced engines are not well established. Expansion of the current nonlinear control strategies to such systems will provide valuable insight not only for internal combustion engine applications but other complex system structures in areas such as robotics and hybrid vehicles.

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