We describe an all-electronic, label-free, resistive-pulse sensor that utilizes multiple microchannels for parallel detection, counting and differentiation of multiple biological particles simultaneously. Four particle solutions, including 20 µm and 40 µm polymethacrylate particles, Juniper Scopulorum (Rocky Mountain Juniper) pollen and Populus deltidoes (Eastern Cottonwood) pollen, were loaded to the four peripheral reservoirs, respectively, and were driven to the central reservoir through four microchannels, all operating simultaneously for particle detection and counting. Experiments demonstrated that this sensor was able to differentiate and count multiple particle solutions simultaneously through its four microchannels fabricated on polymer membranes. Thus the sensing throughput has been improved significantly in contrast to typical Coulter counters without sacrificing accuracy, sensitivity and reliability. Furthermore, the experimental results also proved the feasibility of differentiating various pollens from polymethacrylate microparticles with the multi-channel resistive-pulse sensor. The differentiation is based on difference in size and surface charge for the bioparticles, with no need for labeling of samples. Possible improvements and extensions to other biological particle detection are discussed.

A process based on discrete wavelet transforms is developed for denoising and baseline correction of measured signals from Coulter counters. Given signals from a particular Coulter counting experiment, which detect passage of particles through a fluid-filled microchannel, the process uses a cross-validation procedure to pick appropriate parameters for signal denoising; these parameters include the choice of the particular wavelet, the number of levels of decomposition, the threshold value and the threshold strategy. The process is demonstrated on simulated and experimental single channel data obtained from a particular multi-channel Coulter counter processing. For these example experimental signals from 20 µm polymethacrylate and Cottonwood/Eastern Deltoid pollen particles and the simulated signals, denoising is aimed at removing Gaussian white noise, 60 Hz power line interference and low frequency baseline drift. The process can be easily adapted for other Coulter counters and other sources of noise. Overall, wavelets are presented as a tool to aid in accurate detection of particles in Coulter counters.

Rainfall is one of the most important natural input resources to crop production and its occurrence and distribution is erratic, temporal and spatial variations in nature. Most of the hydrological events occurring as natural phenomena are observed only once. One of the important problem in hydrology deals with the interpreting past records of hydrological event in terms of future probabilities of occurrence. Rainfall analysis is a prerequisite for proper designing of any soil and water conservation structure. Daily rainfall data will be collected from Department of Agronomy, College of Agriculture, Kolhapur for the year 2012-13. For this study Normal, Log-normal and Gumbel distributions of probability are used. From the analysis it was concluded that, Log-pearson type III distribution was found to be good for probability distribution of rainfall in the Kolhapur region.