Moisture isotherms and heat of desorption of canola

 Hamid Reza Gazor(Agricultural Engineering Research Institute.  POB: 31585-845 Karaj- Iran) Abstract: Moisture desorption isotherms (EMC/ERH) of canola cultivar of option 500 determined at 30, 40, 50 and 60℃ using the standard gravimetric static method over a range of relative humidity from 11% to 81%.  The experimental desorption curves were fitted by four equations: modified Henderson, modified Chung–Pfost, modified Halsey and modified Oswin.  The modified Oswin model was found to be a better model to describe the desorption isotherms of canola.  The net isosteric heats of desorption of canola were determined from the equilibrium data at different temperatures.  The net isosteric heat of desorption of canola varied between 1.58–10.41 kJ/mol at moisture varying between 1.0%–12.5% d.b.Keywords: canola, desorption isotherms, isosteric heat of desorption Citation: Hamid Reza Gazor.  Moisture isotherms and heat of desorption of canola.  Agric Eng Int: CIGR Journal, 2010, 12(2): 79-84. &nbsp

Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by Predictive Level Comparisons

In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based Compressive Sensing (CS) algorithms, our proposed algorithm gradually estimates and follows slow changes in the sparse components utilizing a sliding-window technique. We also consider the scenario in which possible flipping errors in the sign bits propagate along iterations (due to the feedback loop) during reconstruction. We propose an iterative error correction algorithm to cope with this error propagation phenomenon considering a binary-sparse occurrence model on the error sequence. Simulation results show effective performance of the proposed scheme in comparison with the literature