Adaptive Integrated Microsystems Laboratory
Research Projects
Motivation for integrating learning with micro-nano sensors (pdf slides)
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Adaptive Integrated Microsystems Laboratory |
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Research Projects Motivation for integrating learning with micro-nano sensors (pdf slides) |
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Micro-data mining With advances in fabrication technology, an ever increasing number of sensors can now be integrated within a single package. Examples of such sensors include microelectrode arrays and microphone arrays. In a classical data acquisition paradigm each sensor channel is treated independently with regard to analog-to-digital conversion and any intelligent processing is delegated to a backend digital signal processor. However, in micro/nano-scale high-density sensor arrays, the detrimental effect of finite resolution on signal processing cannot be ignored, especially if the objective is to mine hidden/weak signatures in high-dimensional analog data. Our aim is to explore techniques for integrating machine learning algorithms directly with analog-to-digital conversion such that resolution can be dynamically allocated across sensor channels to achieve optimal system performance. PDF slides (High-dimensional Delta-Sigma Converters) PDF slides (Auditory surveillance) Keywords: Neural signal recording, Acoustic Surveillance, Intelligent Hearing Aids, high-dimensional analog-to-digital converters (ADC), MIMO ADC Students: Amit Gore, Amin Fazel
In analog and digital VLSI, design methods typically follow a top-down approach where proven algorithms are mapped onto optimized computing hardware. In this research, we are investigating a bottom-up approach where computing paradigms are inherent in hardware can be used for designing specialized algorithms. This is particularly relevant for analog VLSI (aVLSI) where the computational hardware is always approximate and is heavily dependent on the device physics. Our objective is to use universal conservation principles (charge, mass or current) to approximate well known functions and then suitably modify the algorithms to achieve the desired or even superior performance. Our vision is to construct a universal analog processor that is operational across technology nodes and nature of devices uses. PDF slides I (Margin propagation analog decoders) PDF slides II (Nano-powered integrated circuits) Keywords: Margin propagation, nano-power circuits, sub-threshold circuits, nano-power, floating-gate circuits, stochastic resonance circuits Students: Paul Kucher Computational Biosensing Environmental variability and stochastic interaction between bio-molecules are major causes that affect the reliability of existing and emerging biosensors. In this project our objective is to develop hybrid bio-CMOS techniques, that combines sensitivity of biological sensors with reliable computation on silicon to improve the performance of existing biosensor. As a model, we have chosen a disposable polyaniline based immunosensor that is relatively easy to fabricate and test. Our approach is to use source-channel coding principles to embed forward error-correction on the biosensor and decode the output produced by the sensor using a reliable silicon based processor. Keywords: bio-molecular transistor, bioCAD, hybrid bio-CMOS design, forward error-correction, silicon decoders Students: Yang Liu Self-powered biomechanical implants and circuits In this project we are investigating circuit and sensor topologies that can harvest the power directly from the signal being sensed. As a result such circuits and systems can be used for long-term surveillance and monitoring where batteries are impossible to deploy and remote power delivery methods are impractical. In particular, we are developing floating-gate circuits that can be used for long-term health monitoring of biomechanical implants (for example, hip or knee implants) with operational life greater than 10 years. PDF slides (Self-powered floating gate sensor) PDF slides (PLLA-Carbon Nanotubes strain sensor) Keywords: biomechanics, strain sensing, fatigue, self-powering, nano-power circuits Students: Nizar Lajnef, Chengling Huang. Learning algorithms We are investigating real-time learning algorithms that are amenable to hardware implementation and that can deliver robust performance in presence of noise and interference. Our emphasis is primarily on speech processing systems where we are investigating integration of a) source separation algorithms, b) robust feature extraction and c) large margin recognition and classification. PDF slides Keywords: Independent component analysis, speech processing, speaker verification, microphone array, speech recognition, support vector machine. Students: Amin Fazel
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Questions and comments: shantanu at msu dot edu |