Partitioning the impacts of streamflow and evaporation uncertainty on the operations of multipurpose reservoirs in arid regions

Ongoing changes in global climate are expected to alter the hydrologic regime of many river basins worldwide, expanding historically observed variability as well as increasing the frequency and intensity of extreme events. Understanding the vulnerabilities of water systems under such uncertain and variable hydrologic conditions is key to supporting strategic planning and design adaptation options. In this paper, we contribute a multiobjective assessment of the impacts of hydrologic uncertainty on the operations of multipurpose water reservoirs systems in arid climates. We focus our analysis on the Dez and Karoun river system in Iran, which is responsible for the production of more than 20% of the total hydropower generation of the country. A system of dams controls most of the water flowing to the lower part of the basin, where irrigation and domestic supply are strategic objectives, along with flood protection.We first design the optimal operations of the system using observed inflows and evaporation rates. Then, we simulate the resulting solutions over different ensembles of stochastic hydrology to partition the impacts of streamflow and evaporation uncertainty. Numerical results show that system operations are extremely sensitive to alterations of both uncertainty sources. In particular, we show that in this arid river basin, long-term objectives are mainly vulnerable to inflow uncertainty, whereas evaporation rate uncertainty mostly affects short-term objectives. Our results suggest that local water authorities should properly characterize hydrologic uncertainty in the design of future operations of the expanded network of reservoirs, possibly also investing in the improvement of the existing monitoring network to obtain more reliable data for modeling streamflow and evaporation processes

Data acquisition and imaging using wavelet transform: a new path for high speed transient force microscopy

The unique ability of Atomic Force Microscopy (AFM) to image, manipulate and characterize materials at the nanoscale has made it a remarkable tool in nanotechnology. In dynamic AFM, acquisition and processing of the photodetector signal originating from probe–sample interaction is a critical step in data analysis and measurements. However, details of such interaction including its nonlinearity and dynamics of the sample surface are limited due to the ultimately bounded bandwidth and limited time scales of data processing electronics of standard AFM. Similarly, transient details of the AFM probe's cantilever signal are lost due to averaging of data by techniques which correlate the frequency spectrum of the captured data with a temporally invariant physical system. Here, we introduce a fundamentally new approach for dynamic AFM data acquisition and imaging based on applying the wavelet transform on the data stream from the photodetector. This approach provides the opportunity for exploration of the transient response of the cantilever, analysis and imaging of the dynamics of amplitude and phase of the signals captured from the photodetector. Furthermore, it can be used for the control of AFM which would yield increased imaging speed. Hence the proposed method opens a pathway for high-speed transient force microscopy

Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm

Epilepsy is a disorder of the nervous system that can affect people of any age group. With roughly 50 million people worldwide diagnosed with the disorder, it is one of the most common neurological disorders. The EEG is an indispensable tool for diagnosis of epileptic seizures in an ideal case, as brain waves from an epileptic person will present distinct abnormalities. However, in real world situations there will often be biological and electrical noise interference, as well as the issue of a multichannel signal, which introduce a great challenge for seizure detection. For this study, the Temple University Hospital (TUH) EEG Seizure Corpus dataset was used. This paper proposes a novel channel selection method which isolates different frequency ranges within five channels. This is based upon the frequencies at which normal brain waveforms exhibit. A one second window was selected, with a 0.5 second overlap. Wavelet signal denoising was performed using Daubechies 4 wavelet decomposition, thresholding was applied using minimax soft thresholding criteria. Filter banking was used to localise frequency ranges from five specific channels. Statistical features were then derived from the outputs. After performing bagged tree classification using 500 learners, a test accuracy of 0.82 was achieved.Comment: 8 pages, 6 figures, accepted for publication at the 13th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC