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Collaboration Opportunities

The Electrical and Computer Engineering Department at Boise State is your regional partner of choice in communications research, digital system design, energy and power systems, integrated circuits, nanoscale devices and materials, plasma and vacuum electronics, and signal processing. The vast majority of our research is funded by federal government, foundation, and corporate sponsors. Our teams engage actively with national and international research communities and frequently pursue interdisciplinary research with industry partners. Below is a topical overview of some of the transdisciplinary research underway.

Amorphous Electronics

We design new electronic components that could replace some of the existing higher-power consumption components in portable electronic devices. In addition, we create new types of electronic devices that may eventually lead to different computing architectures. We explore significant changes to the way computers process information, including the ability of a computer to make independent decisions based on internal and external inputs. We have developed a back-end-of-line fabrication process that allows our novel device materials to be integrated into a CMOS-based test chip or platform. We perform complete electrical characterization in our laboratory with variable temperature, frequency, optical, and magnetic field capabilities. Faculty: Kris Campbell (ECE)

CMOS Photonics

The rise of “big data” and a profusion of network-intensive applications such as cloud computing, HD multimedia streaming, and data mining, have motivated technologies that manage skyrocketing data rates between chips, motherboards, computers, and networks. Conventional electrical signaling over copper channels is severely limited by attenuation, dispersion, and crosstalk. To alleviate this bottleneck, we are researching CMOS photonics integrated circuits which employ light in a guided medium to enable >1 Tbps on-chip interconnects for multi-core processors, datalinks for “green” data centers, and photonics-enabled RF and biochemical sensing. We focus on modeling photonic components on a system-level design, high-speed mixed-signal and microwave circuit design, and CMOS photonics system packaging and testing. Faculty: Wan Kuang (ECE)

Electrical Energy Storage

A rapid rise in energy consumption fueled by economic growth and population expansion has intensified electrical energy storage technology research. Such efforts will promote a sustainable future by reducing imported fossil fuel dependence and greenhouse gas emissions. Electrical energy storage technologies can power a wide range of devices such as portable electronics, power tools, and electric vehicles, and can also enable application development for “load leveling” of renewable wind and solar energy sources. Our research focuses on developing new nanoarchitectures for rechargeable batteries (e.g., Li and Na). Students studying energy nanomaterials will gain hands-on experience in materials synthesis and characterization, electrochemical processing, coin-cell battery construction, and electrochemical characterization. Faculty: Claire Xiong (MSE)

Multifunctional Nanocomposites

We study the electrical and thermal transport properties of electronic nanomaterials such as carbon nanotubes and graphene. Using this fundamental understanding, we aim to design energy-efficient transistors for post-10 nm silicon CMOS computing, as well as multifunctional nanocomposites for applications in the energy, healthcare, and aerospace industries. This research focuses on: (1) high-volume manufacturing of atomically thin metallic, semiconducting, and insulating nanomaterials; (2) novel methods of material characterization; (3) material design with tailored electrical, thermal, thermoelectric, and electrochemical properties; (4) understanding electronic transport in nanoscale transistors made from emerging nanomaterials; and (5) predicting the effects of nanomaterials on water quality, our food supply, and human health. Faculty: David Estrada (MSE), Elton Graugnard (MSE), Kurtis Cantley (ECE), and Yanliang Zhang (MBE)

Nanoionic Electronics

Nanoionic research deals with devices based on ion-related processes such as conductive bridge memristors and radiation sensors. These emerging technologies will replace today’s non-volatile flash devices to address the increasing scaling problems. Our research answers questions related to new memory solutions, formation of nanoionic memristor arrays for logic functions, and improvement of memristor radiation hardness, speed, and stability. The research focuses on: (1) the design of memristors/arrays and sensors; (2) new material and device structure solutions; (3) radiation studies and sensing; and (4) new architectures for cognitive systems. Students working in this area gain experience in electrical and materials engineering, device testing, materials characterization, and cognitive systems implementation. Faculty: Maria Mitkova (ECE), Nader Rafla (ECE)


Nanophotonics research scales optical devices and components to their ultimate size limits. We envision technologies of the next decade as devices continue to add functionality and become smaller and less expensive. We ask, “How small can a device be?” Our team designs and makes devices that exploit near-field optical interactions on a scale well below the diffraction limit. The research focuses on: (1) using DNA origami (DNA folding) as an assembly template for device fabrication; (2) applying ultrafast laser-based coherent microscopy and spectroscopy for device characterization; and (3) integrating with current semiconductor fabrication methods. Students studying nanophotonics gain multi-disciplinary skills in electrical engineering, optical engineering, physics, quantum electronics, materials engineering, chemistry, and biophysics. Faculty: Wan Kuang (ECE), Elton Graugnard (MSE), Bernard Yurke (MSE), and William Knowlton (MSE)

Thermal-Electric Devices

Our research aims to develop novel materials and energy systems with significantly enhanced energy efficiency. For example, we examine how to improve fuel economy in cars and also reduce their emissions. To achieve our goals, we develop methods to study thermal and energy transport and conversion in nanostructured materials. These methods enable us to design predictable novel material of unprecedented performance. We also develop devices and systems for energy harvesting, thermal management, and biomedical applications. A representative ongoing project is automotive waste heat recovery using a nanomaterials-based thermoelectric generator. Students participating in this research will be exposed to cutting-edge nanoscale science and advanced technology for practical applications. Faculty: Yanliang Zhang (MBE)

Animal Biometrics

Wildlife biologists conduct demography studies to identify individual animals and endangered species in wild populations. To do so, they capture animals on multiple occasions and compare specimen changes over time. Photographs of animals —such as frogs— were taken over days, weeks, months, or years with the intent to distinguish an image of a recaptured frog to images of “new” specimens. Our research simplifies this task by providing tools that automatically compare animals based on photographic images. These tools are based on both traditional methods (PCA— principal components analysis) and modern methods (deep learning via convolutional neural networks). Faculty: John Chiasson (ECE)

Bio-molecular Data Processing

Genome sequencing projects such as the Human Genome Project have generated a huge amount of data that continues to expand exponentially. Comparing this amount of human data against that of other organisms may lead to cures for many human diseases, such as cancers. However, such an effort requires considerable computing resources. Our research addresses this issue by using dedicated hardware and software approaches to handle large and complex molecular biological databases. Methods to improve the speed and efficacy of database search include fast application-specific hardware approaches employing ASICs, FPGAs or GPUs. Software methods include statistical model based successive database filtering and integration of laboratory data on non-coding RNA molecules. Faculty: Jennifer Smith (ECE)

Compressive Sensing

We are researching a revolutionary approach for sampling large visual and audio data. Consider how most people can rapidly pick out a tree from a sky, visually sampling details to infer the whole. Compressive sensing is an approach where the processor need not capture the whole data. Instead, as data enters the device, the processor intelligently determines the next samplings. As a result, this method captures data at a lower sampling rate while maintaining the same quality. Our research examines the impact of compressive sensing on inference problems such as facial recognition and on developing efficient compressive sensing algorithms to identify the lowest sampling rate possible. Faculty: Hao Chen (ECE) and Elisa Barney Smith (ECE).

Image Processing

Our image processing research focuses on document and biomedical applications. We convert images of documents into text you can edit (MS Word) or search (Google), and develop ways to improve low-quality image conversion. We also address image binarization and recognition, develop open-source document analysis software, and convert historical document collections to make them more accessible to scholars. In addition, we build tools for medical practitioners to better enable diagnosis and improve therapy selection. An example is registering (aligning) 2D fluoroscopy image sequences and 3D computed tomography or magnetic resonance images to produce 3D motion clips. Many projects involve collaboration with researchers in biology, radiological science, community & environmental health, and kinesiology. Faculty: Elisa Barney Smith (ECE)

Real-world Signal Processing

Our research focuses on leveraging rapidly increasing device capabilities while minimizing energy usage. Today we carry unprecedented computer power in our pockets. As device functionality increases, so do device energy needs. To address this area, we primarily study how to improve the algorithm design behind basic device functions, minimizing algorithm complexity, and reducing computational load. We use multi-core real-time targets and graphical processing units to develop innovative solutions to a wide variety of real-world signal processing problems. Software-defined radio applications are of particular interest. Faculty: Thad Welch (ECE)

Signal Processing and Communications

Distributed systems enable individuals and organizations to operate at different locations, where they can sense, monitor, communicate, control, and process information. Such systems include wireless sensor networks, relay networks, and distributed data centers. In our research, we design and develop both signal processing and communication algorithms to provide accurate status monitoring, improved communication throughput, and faster, more precise control. Our research focuses on (1) optimal distributed inference algorithm development under resource constraints such as communication bandwidth; (2) joint spectrum sensing and allocation for cognitive radio networks to maximize the spectrum utility; and (3) addressing security issues in distributed systems. Faculty: Hao Chen (ECE)

Neuromorphic Computing

Since their invention in the 1940s, computers have become an
integral part of our daily lives enabling everything from a routine text
message to weather prediction that demands immense supercomputing resources.
However, the processing power of today’s computers pales in comparison to that
most advanced processor — the human brain. Technological progress now enables
us to take up the challenge of developing a new kind of computing architecture
that functions more like the brain. System architectures emulating biological
learning mechanisms may lead to significant increases in speed at low power
consumption suitable for use in many application areas. Three research groups
bring their unique approaches to this area.

Neuromorphic Architectures

We are investigating the integration of memristor arrays with CMOS transistor structures to implement a bio-inspired or neuromorphic design, where the memristors simulate synapses (weights), and CMOS structures form the neural soma or summing nodes. This new architecture forms the basis for an adaptive bio-inspired processor, which has application in a variety of fields. Faculty: Maria Mitkova (ECE) and Nader Rafla (ECE)

Neuromorphic Circuits

The goal of neuromorphic circuit research is to realize monolithically integrated bio-mimetic and bioinspired neuromorphic systems-on-a-chip (SoCs). The research approach is multidisciplinary, including research on developing chalcogenide-based memristive devices, neural network learning and mixed-signal circuit design, chip tape-out, and testing. We have developed neuromorphic circuit prototypes and are working to develop neuromorphic systems and demonstrate algorithms. The systems use memristive devices fabricated at Boise State as weights between neurons. The networks are capable of learning, computing, pattern recognition, and classification. Faculty: Elisa Barney Smith (ECE), Kris Campbell (ECE)

Neuromorphic Interfaces

This research area aims to combine the unique properties and capabilities of neuromorphic architectures with nanoscale systems that interface directly with the environment. Creating mechanically flexible circuits that can conform to many different surface topologies including that of biological tissue is a particular focus. Examples include arrays of pressure and thermally sensitive devices for artificial robotic skin, chemical sensor arrays (artificial olfaction), and electrode arrays for neural interfaces. Faculty: Kurtis Cantley (ECE)

Microwave Vacuum Electronics

Microwave vacuum electron devices are used in a variety of applications ranging from radar to satellite communications, particularly when users need high power density, high efficiency, high reliability, and a long lifetime in harsh space environments. Such devices will be needed for many decades to come. Our research focuses on new-generation devices which use gated vacuum field emission arrays as the electron source. These spatially addressable arrays can improve performance in crossed field amplifiers and magnetrons by controlling electron current injection. We are also interested in electron hop funnels for use in microwave devices and for the study of secondary electron emission from dielectric surfaces. Faculty: Jim Browning (ECE)

Plasma Devices

Plasma is an ionized gas, and as the fourth state of matter, offers unusual properties in many devices and applications. We numerically and experimentally study miniature ion thrusters, which can be used for satellite station keeping. Ion thrusters produce small levels of thrust by accelerating ions using electric fields. They are ideal for long missions such as adjusting orbits or deep space exploration. We also examine how to use microplasma discharges for medicine. Plasmas are gaining considerable interest for their ability to break down gases into free radicals and chemical species that can be used to treat skin diseases and wounds and decontaminate surfaces. Faculty: Jim Browning (ECE), Don Plumlee (MBE), and Ken Cornell (Chemistry)

Energy and Power Systems

We do research in power and energy systems with a particular interest in environmentally sustainable electrical energy generation, transmission, distribution, and utilization. Our research in electrical energy generation includes developing new electric machine theories; parameter identification of large synchronous generators using nonlinear identification methods; modeling and simulation of wind farms in power distribution systems; and power electronic converter design for photovoltaic systems. We are also interested in integrating signal processing and communication technologies for the next-generation smart grid. Finally, our research in electrical energy utilization is currently focused on the FPGA implementation of high-performance electric drives. Faculty: Said Ahmed-Zaid (ECE), Hao Chen (ECE), John Chiasson (ECE), Nader Rafla (ECE), Thad Welch (ECE), and John Gardner (MBE).

Sensor Systems

Our research aims at designing improved sensors and sensor systems for applications such as monitoring airline cabins, remote-monitoring of spent nuclear fuel canisters, wildlife tracking, and tracking of personal health data. The amount of spent nuclear fuel kept in long-term storage is expected to double within the next decade, so our work is particularly urgent and timely. Sensor systems consist of microprocessors, sensors, power management, and data storage, supported by wired or wireless communications. We focus on improving circuit and firmware design in power- and processing-limited environments, optimizing performance under environmental constraints such as intense heat and gamma radiation, and “harvesting” power from energy-rich environments. Faculty: Sin Ming Loo (ECE)

Wireless Sensor Networks

Research in wireless sensor networks takes a more precise look at one aspect of sensor system design, enabling scientists to acquire important data in environments and with a dimensionality that is difficult if not impossible to acquire with traditional instrumentation. A primary focus for our team is on developing a novel data sharing system for wireless sensor networks to facilitate in-network collaborative processing of sensor data. This is important since shifting data processing to the sensor network itself can greatly reduce power requirements and latency issues intrinsic to traditional wireless sensor network data processing methods. Faculty: Sin Ming Loo (ECE)

Remote Sensing of the Environment

Our research focuses on using LiDAR (light detection and ranging), hyperspectral, and multispectral remote sensing to characterize semiarid ecosystems. We are particularly interested in developing methods for quantifying terrain (soil and vegetation) at multiple scales with ground, airborne, and satellite-based LiDAR. Our studies also use hyperspectral imagery for improving ecological structure and function and modeling. Research in our lab also focuses on the development of point cloud analysis tools, visualization of point cloud and hyperspectral data fusion, and unmanned aerial systems (UAS). Faculty: Nancy Glenn (Geosciences)

Reconfigurable Hardware

This research focuses on the integration of processors, reconfigurable logic, and interfaces into a system to provide flexible high-speed solutions to applications requiring complex computations, high-speed processing, and large resource utilization. We investigate the use of reconfiguration capabilities of Systems on a Programmable Chip to create intelligent architectures for use in real-world application areas.  Current research projects include: 1) collective adaptive systems; 2) health diagnosis and monitoring systems;  3) study of cognitive processing; and 4) evolvable and developmental hardware. Faculty: Nader Rafla (ECE)