Method of Detecting System Function by Measuring Frequency Response

Real time battery impedance spectrum is acquired using one time record, Compensated Synchronous Detection (CSD). This parallel method enables battery diagnostics. The excitation current to a test battery is a sum of equal amplitude sin waves of a few frequencies spread over range of interest. The time profile of this signal has duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known, synchronous detection processes the time record and each component, both magnitude and phase, is obtained. For compensation, the components, except the one of interest, are reassembled in the time domain. The resulting signal is subtracted from the original signal and the component of interest is synchronously detected. This process is repeated for each component

Method of detecting system function by measuring frequency response

Real-time battery impedance spectrum is acquired using a one-time record. Fast Summation Transformation (FST) is a parallel method of acquiring a real-time battery impedance spectrum using a one-time record that enables battery diagnostics. An excitation current to a battery is a sum of equal amplitude sine waves of frequencies that are octave harmonics spread over a range of interest. A sample frequency is also octave and harmonically related to all frequencies in the sum. The time profile of this signal has a duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known and octave and harmonically related, a simple algorithm, FST, processes the time record by rectifying relative to the sine and cosine of each frequency. Another algorithm yields real and imaginary components for each frequency

Using Moored Arrays and Hyperspectral Aerial Imagery to Develop Nutrient Criteria for New Hampshire\u27s Estuaries

Increasing nitrogen concentrations and declining eelgrass beds in Great Bay, NH are clear indicators of impending problems for the state’s estuaries. A workgroup established in 2005 by the NH Department of Environmental Services and the NH Estuaries Project (NHEP) adopted eelgrass survival as the water quality target for nutrient criteria development for NH’s estuaries. In 2007, the NHEP received a grant from the U.S. Environmental Protection Agency to collect water quality information including that from moored sensors and hyper-spectral imagery data of the Great Bay Estuary. Data from the Great Bay Coastal Buoy, part of the regional Integrated Ocean Observing System (IOOS), were used to derive a multivariate model of water clarity with phytoplankton, Colored Dissolved Organic Matter (CDOM), and non-algal particles. Non-algal particles include both inorganic and organic matter. Most of the temporal variability in the diffuse attenuation coefficient of Photosynthetically Available Radiation (PAR) was associated with non-algal particles. However, on a mean daily basis non-algal particles and CDOM contributed a similar fraction (~30 %) to the attenuation of light. The contribution of phytoplankton was about a third of the other two optically important constituents. CDOM concentrations varied with salinity and magnitude of riverine inputs demonstrating its terrestrial origin. Non-algal particle concentration also varied with river flow but also wind driven resuspension. Twelve of the NHEP estuarine assessment zones were observed with the hyperspectral aerial imagery on August 29 and October 17. A concurrent in situ effort included buoy measurements, continuous along-track sampling, discrete water grab samples, and vertical profiles of light attenuation. PAR effective attenuation coefficients retrieved from deep water regions in the imagery agreed well with in-situ observations. Water clarity was lower and optically important constituent concentrations were higher in the tributaries. Eelgrass survival depth, estimated as the depth at which 22% of surface light was available, ranged from less than half a meter to over two meters. The best water clarity was found in the Great Bay (GB), Little Bay (LB), and Lower Piscataqua River (LPR) assessment zones. Absence of eelgrass from these zones would indicate controlling factors other than water clarity

Cell-Type Specific Changes in Glial Morphology and Glucocorticoid Expression During Stress and Aging in the Medial Prefrontal Cortex.

Repeated exposure to stressors is known to produce large-scale remodeling of neurons within the prefrontal cortex (PFC). Recent work suggests stress-related forms of structural plasticity can interact with aging to drive distinct patterns of pyramidal cell morphological changes. However, little is known about how other cellular components within PFC might be affected by these challenges. Here, we examined the effects of stress exposure and aging on medial prefrontal cortical glial subpopulations. Interestingly, we found no changes in glial morphology with stress exposure but a profound morphological change with aging. Furthermore, we found an upregulation of non-nuclear glucocorticoid receptors (GR) with aging, while nuclear levels remained largely unaffected. Both changes are selective for microglia, with no stress or aging effect found in astrocytes. Lastly, we show that the changes found within microglia inversely correlated with the density of dendritic spines on layer III pyramidal cells. These findings suggest microglia play a selective role in synaptic health within the aging brain

Acquiring Impedance Spectra From Diode-Coupled Primary Batteries to Determine Health and State of Charge

The U.S. Army uses BA5590 Lithium Sulfur Dioxide

Method of Detecting System Function by Measuring Frequency Response

Real-time battery impedance spectrum is acquired using a one-time record. Fast Summation Transformation (FST) is a parallel method of acquiring a real-time battery impedance spectrum using a one-time record that enables battery diagnostics. An excitation current to a battery is a sum of equal amplitude sine waves of frequencies that are octave harmonics spread over a range of interest. A sample frequency is also octave and harmonically related to all frequencies in the sum. The time profile of this signal has a duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known and octave and harmonically related, a simple algorithm, FST, processes the time record by rectifying relative to the sine and cosine of each frequency. Another algorithm yields real and imaginary components for each frequency

Method of Estimating Pulse Response Using an Impedance Spectrum

Electrochemical Impedance Spectrum data are used to predict pulse performace of an energy storage device. The impedance spectrum may be obtained in-situ. A simulation waveform includes a pulse wave with a period greater than or equal to the lowest frequency used in the impedance measurement. Fourier series coefficients of the pulse train can be obtained. The number of harmonic constituents in the Fourier series are selected so as to appropriately resolve the response, but the maximum frequency should be less than or equal to the highest frequency used in the impedance measurement. Using a current pulse as an example, the Fourier coefficients of the pulse are multiplied by the impedance spectrum at corresponding frequencies to obtain Fourier coefficients of the voltage response to the desired pulse. The Fourier coefficients of the response are then summed as reassembled to obtain the overall time domain estimate of the voltage using series analysis