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Extensive research has not been done on propagation modeling for natural short-and tall-grass environments for the purpose of wireless sensor deployment. This study is essential for efficiently deploying wireless sensors in different applications such as tracking the grazing habits of cows on the grass or monitoring sporting activities. This study proposes empirical path loss models for wireless sensor deployments in grass environments. The proposed models are compared with theoretical models to demonstrate their inaccuracy in predicting path loss between sensor nodes deployed in natural grass environments. Results show that theoretical models deviate from the proposed models by 12 to 42%. Also, results of the proposed models are compared with experimental results obtained from similar natural grassy terrains at different locations resulting in similar outcomes. Finally, the results of the proposed models are compared with previous studies and other terrain models such as those in dense tree environments. These comparisons show that there is significant difference in path loss and empirical models' parameters. The proposed models, as well as the measured data, can be used for efficient planning and future deployments of wireless sensor networks in similar grass terrains.Index Terms-path loss model, RF propagation, short and tall natural grass, terrain, terrain factor, wireless sensor network, XBee radio.

We have investigated the use of a time-domain optimal filtering method to simultaneously minimize both the baseline variation and high-frequency noise in near-infrared (NIR) spectrophotometric absorption data of glucose dissolved in a simple aqueous (deionized water) matrix. By coupling a third-order (six-pole) digital Butterworth bandpass filter with partial least-squares (PLS) regression modeling, glucose concentrations were determined for a set of test data with a standard error of prediction (SEP) of 10.53 mg/dl (mean percent error: 4.24%) using seven PLS factors. Compared to the unfiltered test data for six PLS factors and a SEP = 17.00 (mean percent error: 7.38%) this results shows more than a 38% decrease in the error. The glucose concentrations ranged from 51 mg/dl to 493 mg/dl, and the NIR spectral region between 2088 nm and 2354 nm (4789 cm-1 and 4248 cm-1) was used to develop the optimal PLS model. The optimal PLS model was determined from a sequence of three-dimensional performance response maps for different numbers of PLS factors (2-10). A total of 99 NIR spectra were generated for glucose dissolved in deionized water using a NIRsystems 5000 dispersive spectrophotometer. Nine of these spectra were generated for only water, which were averaged and subtracted from the remaining 90 spectra to generate the training and test data sets, thereby, removing the intrinsic high background absorption due to the water. The training set consisted of 57 spectra and associated glucose concentration target values, and the test set was comprised of the remaining 33 spectra and target values. Performance results were compared for three different digital Butterworth bandpass filters (four-poles, six-poles, and eight-poles), and a digital Gaussian filter design approach (i.e., Fourier filtering).

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