Research

The objective of this research is to study the fundamental interactions and influences that individual components of a power system have on each other and the overall effect of these interactions on system performance. The approach uses a game-theoretic model of the power system to develop a framework for a new class of distributed control methods. In this new model, system components use energy resources to compete for local and global objectives such as voltage, current, power, or energy.

The intellectual merit is of this work is in its potential to improve control of interconnected power components. Control has typically been one of two extremes: either completely localized control (focused on local objectives) or a centralized control scheme requiring extensive communications, infrastructure, and a single-point of failure. The proposed approach considers how each different local objective interacts across the system. This will result in an operating point that is a global balance of local objectives. This work will determine how the fundamental interactions of the components contribute to, or degrade from, the higher-level objectives of the overall system, such as stability, survivability, and efficiency.

The broader impacts will extend to electrical power networks at all levels where more diverse sources and loads are being utilized, and new technologies are being driven by an ever-increasing demand for performance, stability, efficiency, and flexibility. This new approach to energy control and management will strengthen education objectives by enhancing the content of graduate and undergraduate classes, as well as adding meaningful laboratory experiences.

The objective of this research is to increase the detection resolution and reduce the implementation cost of wideband spectrum sensing and cognition, which are challenging core issues in spectrum-sharing cognitive networks. The approach seeks to leverage the benefits of compressive sampling and user cooperation to develop a collaborative compressed sensing framework for wideband networks. Research thrusts include compressed wideband sensing at affordable signal acquisition costs and reliable collaborative sensing with low network overhead.

This research combines compression and collaboration techniques with technical innovations in wideband spectrum sensing. The project seeks to develop a suite of compressed sensing algorithms that exploit spectral sparsity to reduce the need for high-speed sampling in ultra-wideband and wideband radios. A decentralized approach for joint cooperation and compression, which exploits spatial diversity to alleviate the hidden terminal problem caused by wireless channel fading, is investigated for ad hoc cognitive networks. Further, the research examines fundamental tradeoffs in a cooperative sensing system including diversity gain, compression, sensing time and complexity.

This project will lead to new techniques that can improve the spectrum utilization efficiency and user capacity of wireless networks, thus allowing a multitude of new cognitive radio transceivers. Such devices include wireless sensors and radio frequency identification (RFID) chipsets for monitoring and tracking applications. The compressed sensing and decentralized collaboration techniques being investigated also have the potential to contribute to other sensing-related applications, such as wireless sensor networks. Research experiences gained through this project help to prepare students for engineering in the 21st Century.

The objective of this research project is to identify algorithms that leverage recent advances in adaptive sensing for application to Multiple-Input Multiple-Output (MIMO) radar systems. The term adaptive sensing refers to sensing modalities in active Intelligence, Surveillance, and Reconnaissance (ISR) systems that employ feedback to select transmitted waveforms in response to previously processed data, to adaptively optimize overall system performance.  MIMO radar systems are those systems with multiple transmit and receive apertures, and the capability for independent signal selection at each transmit aperture.  We use adaptive sensing for the high-dimensional problem of multiple  signal selection in MIMO radar systems. A complementary problem is that of target tracking based on the selected transmit signals and the optimal statistical processing of the data collected at the receive apertures. Significant improvements in tracking performance using the proposed approach are expected and will be quantified.

The specific objectives of this research are as follows. We first identify algorithms for adaptive sensing and target tracking under a simple point target model. This includes the generalization of existing adaptive sensing results to the adaptive sensing of state vectors with random amplitude, then target tracking using a known propagation channel response, and finally target tracking for an unknown propagation channel response.  Second, we are developing models for what we have termed complex point targets,and based on these models we will develop methods for statistical inference on these targets, including detection, parameter estimation, and tracking.  Third, we will investigate the effects of uncertain local oscillator (LO) synchronization across all the  transmit and receive apertures in a MIMO radar system, which is inevitable given the spatial distribution of the apertures. Methods for mitigating these effects, that can be included in the adaptive sensing and statistical inference algorithms, are being derived.  Finally, we will integrate the results of these  three efforts just described, leading to algorithms for tracking complex point targets using a MIMO radar system and adaptive sensing methodology, taking into account system synchronization issues.

Wireless Body Sensor Networks (BSNs) composed of a hybrid of implantable, ingestible and wearable sensors
have emerged as a new paradigm of digital healthcare for disease alert, drug distribution, and real-time
monitoring of hospitalized as well as non-hospitalized chronically-ill or aged patients. In parallel to advances
in biosensor technology and medical information fusion, the development of BSNs has illuminated major hurdles
in embedded communication and networking, which has to deal with formidable challenges including:
extremely stringent energy constraints imposed by in vivo sensors, very limited on-sensor computing capability,
and uniquely complex radio propagation body environments. The goal of this research project is to design, optimize and test embedded communication and networking techniques for wireless BSNs, in order to accomplish unprecedented energy efficiency, reliability and cost structure.

The goal of this project is to explore novel all-dielectric metamaterials that can provide integrated resonance responses at electromagnetic wave incidence due to interaction between their elements. Metamaterials are artificially engineered structures that exhibit properties not found in constituent materials and even generally not found in nature. Metamaterials are composed of artificial “atoms,” i.e. of sub-systems of functional elements. While natural materials represent multi-atom compounds, metamaterials are built from miniature resonators. First metamaterial structures have been realized from the metal wire and split-ring resonator arrays. The most striking type of metamaterials, negative index materials, possess with left-handed properties and exhibit negative index of refraction. This specific is caused by the fact that both effective material parameters, i.e. the electric permittivity and the magnetic permeability, are negative at frequencies in the pass band providing backward wave propagation and other left-handed phenomena.

Since the first implementation of metal metamaterials with negative refractive index in 2000, their performance was mainly analyzed by using homogenization theory, which did not properly account for the complex effects of inter-resonator coupling, resonance mode splitting and integration of multiple resonance responses. This project aims to reveal the contribution and potential of cooperative resonance phenomena and to develop advantageous all-dielectric alternatives to existing metamaterials. Novel metamaterial functionalities are expected to arise from additional paths for wave transfer due to formation of chains of coupled neighboring resonators and self-organization of coupled electromagnetic fields into networks of different symmetry depending on rotational invariance of the resonance modes and on the type of particle arrays. In addition, all-dielectric metamaterials promise low loss, isotropy, and tunability at frequencies from RF to optics.

The approach of this research is to combine theoretical studies, computational full-wave electromagnetic modeling of multi-element arrays, designing various metamaterial architectures, optimization procedures, and fabrication and testing of the prototypes. Integration of knowledge from different disciplines, employment of powerful simulation tools, and approbation of latest achievements in materials integration will provide deep insight into the nature of wave processes in engineered structures with integrated resonances; advance understanding of the phenomena of enhanced transmission, negative refraction, electromagnetic cloaking and wave localization; and uncover novel metamaterial properties inaccessible in the existing designs.

Development of low-loss all-dielectric metamaterials with new functionalities will trigger innovative solutions for radio frequency, microwave, and photonic devices and systems. The success of this work will impact communication, imaging, medical, and homeland security equipment. Expected advances will enrich research and education opportunities for undergraduate and graduate students and will attract students to the complex field of electromagnetics. The knowledge obtained in course of this project will be incorporated in the undergraduate courses and new interdisciplinary graduate courses. Dissemination of new knowledge will be provided by publications and presentations at domestic and international conferences including the newly organized IEEE workshop “Women in Electromagnetics”.

For the development of the very high-speed high-density nanotechnology integrated circuits crucial for the practical realization of the nanocomputers with unlimited potential for the U.S. space program and the semiconductor industry, it is important to consider new nanoscale quantum devices and interconnections. Carbon nanotubes have emerged as a strong candidate for the interconnections for such next generation circuits. In this one-year research effort, we plan to carry out a feasibility study and identify the various research areas related to the application of carbon nanotubes for the nanotechnology integrated circuits and to lay down the foundation of a research program at Michigan Tech for the next ten years.

A hybrid sensor/actuator network, which consists of a large number of static sensors and relatively small number of mobile robotic sensors, opens new frontiers in a variety of civilian and military applications and in some scientific disciplines. The combination of a wireless sensor network and a multi-robot system reduces the cost but significantly enhances each other's capability. The static wireless sensor network provides collaborative sensing, communication, coordination and navigation to a multi-robot system and human operators. The mobile robots augment sensor networks' capability by their mobility and advanced sensing, communication and computation capability.

The objective of this project is to address two challenging research issues in a hybrid sensor/actuator network: autonomous sensor/actuator coordination algorithms and self-configuring communication protocols. The specific goals of this research project include: (i) dynamic modeling and organization of hybrid sensor/actuator networks; (ii) sensor/actuator coordination algorithms to reallocate sensing, networking and computing resources to provide required coverage and specified sensing accuracy; (iii) self-configuring protocols to provide information for environmental sensing, communication, robot coordination and navigation; (iv) robot navigation algorithms in sensor networks; and (v) a perceptive reference frame for the analysis and design of the coordination algorithms and communication protocols.