SBIR-STTR Award

Significant Aims: Biomedical research, human performance and other laboratories that work with living subjects and perform complex experiments unanticipated by software "solution" providers, must expend significant resources creating & maintaining data acquisition and control (DAQ&C) systems that are seldom useful in other labs because of their complexity or poor documentation, and quickly become obsolete because of design limitations and hardware dependencies. We have a working prototype of a DAQ&C system, called "LabOS" that [1] is usable at "Operator", "Experimenter" and "Programmer" levels, and provides [2] deterministic ("hard") closed-loop control at kilohertz rates, [3] high speed buffered sampling (eg, for spike profiling), [4] multi-thread, state machine based, data-contingent, experimental protocols, and [5] a modern, richly informative, realtime graphical interface for viewing & controlling ongoing experiments, [6] on multiple platforms, with no dependence on unusual or single-source hardware. We now propose to [1] segment & harden the system so that user modifiable parts are easily & safely reconfigured, and other parts are invisible, [2] generalize certain aspects of LabOS to support its widest possible use, [3] provide data export tools, [4] optimize platform independence [5] produce contextual, reference and tutorial documentation, [6] support a web-based component library, and [7] conduct a beta test program to evaluate the experiences and suggestions of initial users, in an extended development cycle. Methods: LabOS is written in "G" under LabVIEW-RTZ. The LabOS Console runs under Mac OS X, Windows or Linux. The LabOS Server & Controller run under Ardence PharLap ETSZ on any National Instruments? real-time hardware target, including PXI chassis, PCI boards, and some desktop PCs.

Public Health Relevance:
Acquisition of well-controlled, experimental data defines empirical science, and yet, available DAQ&C systems are incapable of supporting complex experiments, require ongoing programmer support, or support only specialized, popular paradigms. A DAQ&C system providing the power and flexibility to perform innovative biomedical experiments would be of great scientific and fiscal value

Significant Aims: Biomedical research, human performance and other laboratories that work with living subjects and perform complex experiments unanticipated by software "solution" providers, must expend significant resources creating & maintaining data acquisition and control (DAQ&C) systems that are seldom useful in other labs because of their complexity or poor documentation, and quickly become obsolete because of design limitations and hardware dependencies. We have a working prototype of a DAQ&C system, called "LabOS" that [1] is usable at "Operator", "Experimenter" and "Programmer" levels, and provides [2] deterministic ("hard") closed-loop control at kilohertz rates, [3] high speed buffered sampling (eg, for spike profiling), [4] multi-thread, state machine based, data-contingent, experimental protocols, and [5] a modern, richly informative, realtime graphical interface for viewing & controlling ongoing experiments, [6] on multiple platforms, with no dependence on unusual or single-source hardware. We now propose to [1] segment & harden the system so that user modifiable parts are easily & safely reconfigured, and other parts are invisible, [2] generalize certain aspects of LabOS to support its widest possible use, [3] provide data export tools, [4] optimize platform independence [5] produce contextual, reference and tutorial documentation, [6] support a web-based component library, and [7] conduct a beta test program to evaluate the experiences and suggestions of initial users, in an extended development cycle. Methods: LabOS is written in "G" under LabVIEW-RT(r). The LabOS Console runs under Mac OS X, Windows or Linux. The LabOS Server & Controller run under Ardence PharLap ETS(r) on any National Instruments(TM) real-time hardware target, including PXI chassis, PCI boards, and some desktop PCs.

Public Health Relevance:
Acquisition of well-controlled, experimental data defines empirical science, and yet, available DAQ&C systems are incapable of supporting complex experiments, require ongoing programmer support, or support only specialized, popular paradigms. A DAQ&C system providing the power and flexibility to perform innovative biomedical experiments would be of great scientific and fiscal value