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Research Areas

Analog and Mixed-Signal IC Design

Analog to Digital Converter (ADC) Design

Boise State’s Mixed Signal IC design group has active research program in development of High-Speed Continuous-time Delta-Sigma Analog-to-Digital Converters (ADCs) and novel reconfigurable and hybrid-ADC architectures. The ADCs are targeted towards next-generation wireless communication systems and software-defined radios (SDRs).

High-speed Interconnects

Research in this area involves developing energy-efficient >10 Gb/s serial links for electrical as wells as optical interconnects. The research involves circuit design for transmitters, receivers, equalizers and clock data recovery (CDR) and PLLs. Integrated CMOS photonics circuit design for next-generation interconnects and on-chip IOs is the primary focus of this research thrust.

Computer Engineering

Sensor System

Sensor nodes are composed of small microprocessors connected to a set of sensors and supported by various communications, power management, and data storage systems. Sensor nodes must often be power efficient as they may need to operate for extended periods without external power. The microprocessors used in embedded systems are inherently limited in comparison to the processors inside desktop computers. Embedded processors give up some overall processing power in exchange for both reduced physical size and reduced power requirements. Current research include FAA airliner cabin environmental monitoring and NIH in-home air quality monitor

Monitoring Spent Nuclear Fuel Containers

The primary goal of the project is to develop a self powered (energy harvesting) sensor system internal to the container as well as providing the resulting sensor information to the end user.

Wireless Sensor Network

Frequently, individual sensor nodes are connected wirelessly to form wireless sensor networks. Networking sensor nodes allows scientists to more accurately characterize phenomenon of interest by providing a means to both temporally and spatially relate sensor data.

This research is currently focused on developing a novel data sharing system for wireless sensor networks to facilitate in-network collaborative processing of sensor data. In the process of developing this system it became clear that perhaps the most expedient way to test many of the ideas was to create a distributed simulation rather than developing directly on the final target embedded hardware. That’s how RPi Cluster came to be.

  • For more information on the area of Raspberry Pi research, please visit the Raspberry Pi information page

Adaptive Hardware and Systems on Reconfigurable Chips

A complete useful adaptive system on FPGA, contains an embedded processor cores along with all other functional hardware components such as memories, input/output and communication interfaces.  Adaptation reflects the capability of a system to maintain or improve its performance in the context of internal or external changes such as environmental changes, interference, modification of requirements, trade-offs between performance and resources. In this research, we investigate the use of partial reconfiguration capabilities of FPGAs, and adaptive and evolvable hardware design techniques and platforms in the design and implementation of such systems. These systems are applied to several real world solutions such as: health diagnosis, networks, recognition, identification, inspection, automation, networks, and control.

Nanoscale Electron Devices

DNA nanotechnology

Boise State’s research on DNA nanotechnology is a collaborative effort, and the work is focused on both DNA origami and DNA catalytic networks and is funded by both DARPA and NSF.

Nano-devices and Materials

Our group explores novel materials and the electrical properties of devices fabricated with those materials. These materials include chalcogenide-based materials used as memristors, as non-volatile memory, and in reconfigurable electronics applications.Materials studied are fabricated or deposited within our group or with other university or industry collaborators. We have several materials characterization instruments in our laboratory, including a microRaman spectrometer, modulated differential scanning calorimeter, n&k tool for film optical properties, atomic force microscope, and a continuous-wave electron spin resonance spectrometer equipped with a dual-mode cavity. For device characterization, we have 4 microprobe stations, two of which are capable of variable temperature operation between 4.2 K to 400 K. We use the Boise State University Idaho microfabrication laboratory (IML) to process devices. In addition, we regularly integrate our materials into the back-end-of-line CMOS processes.

Nanoionic materials and devices

The research work carried out in this group is mainly related to characterization of the structure of chalcogenide glasses doped with Ag or Cu, as well as formation and testing of conductive bridge memristors and arrays based on them. Further aspect of the research work is related to creation of radiation sensors utilizing the radiation sensitivity of chalcogenide glasses (ChG) and radiation induced ions diffusion in them which results in drastic conductivity change.

Neuromorphic Computing

Neuromorphic computing

The neuromorphic computing group is investigating novel system architectures which emulate biological learning mechanisms using memristive devices. The research is multidisciplinary including research on developing chalcogenide-based memristive devices, neural network learning and mixed-signal circuit design, chip tape-out and testing. Our group has developed neuromorphic circuit prototypes and is working to develop and demonstrate neuromorphic systems and algorithms, comprising of the memristive devices, which capable of learning, computing, pattern recognition and classification. The ultimate goal of this research thrust is to realize monolithically integrated bio-mimetic and bio-inspired neuromorphic systems-on-a-chip (SoCs).

Neuromorphic Architectures

Another team of investigators is exploring 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 constitutes a basis of an adaptive bio-inspired processor which has applications in a variety of fields.

Neural Systems and 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 an artificial robotic skin, chemical sensor arrays (artificial olfaction), and electrode arrays for neural interfaces.

Photonics

Nanophotonics

The mission of nanophotonics research is to scale optical devices and components to their ultimate size limits. This usually involves designing near-field optical interactions that guide electromagnetic energy on a scale well below the diffraction limit. The group is currently involved in understanding the electro-optic properties of surface plasmon nanostructures using several numerical and experimental methods. The nanophotonics group also collaborates closely with material scientists in exploring new sub-10 nm fabrication technique using single strand DNA as assembly scaffold.

Integrated silicon photonics

Integrated silicon photonics is an emerging area of research and development and is considered the enabler of yet higher data rates for next-generation computing, ‘green’ energy-efficient data centers and biomedical sensing. The research involves high-speed circuit design which combines the high optical bandwidth silicon photonics with the well-established CMOS signal-processing paradigms, all on the same chip.

Optical Devices and Nanophotonics

The mission of nanophotonics research is to scale optical devices and components to their ultimate size limits. This usually involves designing near-field optical interactions that guide electromagnetic energy on a scale well below the diffraction limit.

Plasma and Vacuum Electron Devices

Microwave vacuum electron devices

Vacuum electron devices are used in a variety of applications from communications to radar systems to medical imaging. Research in this area involves experimental and modeling efforts into electron hop funnels and into Microwave Vacuum Electron Devices (MVEDs) such as magnetrons and Crossed-Field Amplifiers used in high power radar.

Ion thrusters and microplasmas

Electric propulsion systems accelerate mass through the use of electric fields instead of by means of a chemical reaction. The research includes experimental and simulation efforts in development of Inductively Coupled Plasma (ICP) sources for small Ion Thrusters and the study of microplasma transistors.

Power and Energy Systems

Advanced FPGA Implementation of Electric Machine Drives

High-performance electric drives necessitate modern and sophisticated controllers involving fast and extensive computations. FPGAs are widely used in this area of research to emulate different control methodologies and their design verification. In the case of induction machines, various control methodologies such as sensorless Direct Torque Control (DTC), Neural Network (NN) Control and Fuzzy Logic (FL) Control can be implemented and tested on FPGA chips. Current research aims at creating a hybrid FPGA/embedded-microprocessor platform for the rapid prototyping of new control algorithms for the purpose of improving cost, speed, and performance of induction machine drives.

Performance Comparison of a Series-Connected Induction Machine versus a Wound-Rotor Induction Machine

This project aims at investigating the operational characteristics and parameter differences between conventional wound-rotor and series-connected induction machines. The series configuration has been shown to have better voltage regulation and offers the possibility of higher power output capability than that of the wound-rotor configuration in stand-alone operation. The goal of this research is to obtain the characteristic of the mutual inductance versus magnetizing current that will establish the capacitance requirements for self-excitation so that proper simulations can be run to predict the performance behavior of the new generator.

Signal Processing

Document Image Processing

It is common to convert an image of a document into text that can be edited (MS Word) or searched (Google). Several commercial software packages can do this. However, when the image quality is low, the error rate is high. Methods of understanding and compensating for the image quality are the primary topics of interest, but research includes topics from open-source software development to working with historical collections like Melville Marginalia Online.

Biomedical Image Processing

Many different projects involving biomedical images are ongoing. The images include x-ray, CT, MRI and ultrasound. Projects include registration of 2D and 3D images, ultrasound image segmentation, and building segmented 3D models from a series of 2D slices.

Filter design and multi-rate systems

Developing innovative solutions to a wide variety of real-world signal processing problems. These efforts focus not only on meeting specifications but on reducing the computational load/complexity of the required algorithm.

Real-time implementation

Using a variety of commercially available real-time DSP targets, we implement working systems that solve real-world problems.

Multi-core DSP and GPU-based signal processing

Developing algorithms for multi-core real-time DSP targets and GPUs.

Distributed Statistical Inference

Research in this area involves design and develop both signal processing and communication algorithms for distributed systems for sensing, monitoring and control purposes.

Noise Enhanced Signal Processing (NESP)

Realizing the fact that almost all the systems are suboptimal in certain ways, NESP provides a low-cost and adaptive ways to improve system performance. Instead of redesign and re-implement of  new systems, NESP improves the system performance by changing the input to the system via injecting NOISE. Research in this area involves design and develop novel NESP algorithms for emerging applications and exploration of the performance limit of the NESP approaches.

Compressive Sensing (CS) and Pattern Recognition (PR)

Notice that almost all the real world data are highly sparse and compressible, CS is a revolutionary approach for data acquisition with sampling rate much slower than the Nyquist rate without performance loss. In this research, we focus on studying the impact of CS on inference problems such as Pattern Recognition and Estimation and developing efficient CS algorithms to achieve optimal PR performance with lowest sampling rate.