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—A power and area efficient sensor interface consumes 6.4 mW from 1.2 V while occupying 5 mm 5 mm in 0.13 m CMOS. The interface offers simultaneous access to 96 channels of broadband neural data acquired from cortical microelectrodes as part of a head-mounted wireless recording system, enabling basic neuroscience as well as neuroprosthetics research.(More)
The use of high throughput screening (HTS) to identify lead compounds has greatly challenged conventional quantitative structure-activity relationship (QSAR) techniques that typically correlate structural variations in similar compounds with continuous changes in biological activity. A new QSAR-like methodology that can correlate less quantitative assay(More)
A power and area efficient sensor interface consumes 6.5 mW from 1.2 V supplies while occupying 5 mm x 5 mm in 0.13 µm CMOS. The interface enables full bandwidth access to 96 channels of data acquired from cortical microelectrodes as part of a head-mounted wireless recording system for unconstrained primate neuroscience experiments. Open loop amplification(More)
  • Hua Gao
  • 2001
The application of three-dimensional H-suppressed BCUT metrics (BCUTs) in binary QSAR analysis was investigated using carbonic anhydrase II inhibitors and estrogen receptor ligands as test cases. Variable selection was accomplished with a genetic algorithm (GA). Highly predictive binary QSAR models were obtained for both sets of compounds within 200 GA(More)
We focused this work on handling variation in facial appearance caused by 3D head pose. A pose normalization approach based on fitting active appearance models (AAM) on a given face image was investigated. Profile faces with different rotation angles in depth were warped into shape-free frontal view faces. Face recognition experiments were carried out on(More)
In this paper an overview of face recognition research activities at the interACT Research Center is given. The face recognition efforts at the interACT Research Center consist of development of a fast and robust face recognition algorithm and fully automatic face recognition systems that can be deployed for real-life smart interaction applications. The(More)
OBJECTIVE Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of(More)