Programmable Switched Capacitor Finite Impulse Response Filter with Circular Memory Implemented in CMOS 0.18μm Technology

This paper presents a programmable multi-mode finite impulse response (FIR) filter implemented as switched capacitor (SC) technique in CMOS 0.18μm technology. Intended application of the described circuit is in analog base-band filtering in GSM/WCDMA systems. The proposed filter features a regular structure that allows for elimination of some parasitic capacitances, thus significantly improving the filtering accuracy. Due to its modularity that allows for dividing the circuit into two separate sections, the circuit can be easily reconfigured to work as either infinite impulse response (IIR) or as finite impulse (FIR) filter. One of the key components that allows for this multi-mode operation is the proposed programmable and ultra low power multiphase clock circuit. The 24-taps filter for the sampling frequency of 30MHz dissipates power of 4.5mW from a 1.8V suppl

Phytoplankton production in relation to simulated hydro- and thermodynamics during a hydrological wet year – Goczałkowice reservoir (Poland) case study

Phytoplankton is one of the crucial components of water body ecosystems. Its presence and development depend on biological, physical and chemical factors and in consequence it is an important indicator of ecosystem condition. Monitoring of phytoplankton production, measured as chlorophyll a concentration, is a useful tool for assessing the status of dam reservoirs. Modeled chlorophyll a concentrations are used as water quality indicators in locations not included in monitoring systems, in situations when the temporal resolution of the monitoring is not enough, and in assessments of the impacts of future activities. Therefore, the aim of this study was to find correlations between hydro- and thermodynamics and the chlorophyll a concentration for possible application in reservoir monitoring and management, using an ELCOM-CAEDYM model. The analysis included summer and fall which are most prone to algal blooms, and four phytoplankton groups identified as dominant in the reservoir based on periodic observations. Comparisons of simulated water temperature and both observed and simulated chlorophyll a concentrations confirmed that these variables are significantly correlated (correlation of hourly chlorophyll a and water temperature was 0.70, ranging from 0.55 to 0.81 in the bottom and surface water layers, respectively, while for daily outputs it was 0.74, ranging from 0.60 to 0.83). This relation was stronger than that of chlorophyll a to nutrient (N, P and Si) concentrations. What is more, the method used allowed the assessment of a much more detailed spatial and temporal distribution of phytoplankton groups compared with conventional monitoring techniques. The study indicated that the phytoplankton community was dominated by Chlorophytes and Diatoms with a larger share of Chlorophytes in shallow parts of the reservoir. This domination was weaker after short water mixing events in summer and especially after the fall turnover. The increase in phytoplankton diversity was estimated to occur mainly near the surface and in shallow parts of the reservoir. Most of the observed concentrations of individual phytoplankton groups differed from simulation results by less than 25% and the model reflected accurately 74% of observed trends in concentrations. Calculated chlorophyll a concentration was well matched to hourly monitoring data (mean squared error = 5.6, Nash–Sutcliffe model efficiency coefficient = 0.51, Pearson correlation coefficient = 0.72 and p-value = 0.0007). High compatibility of the model to the values measured in the reservoir make it a promising tool for the prediction and planning of actions aimed at maintaining good functioning of the reservoir

Społeczno-ekonomiczne i przestrzenne przemiany struktur regionalnych Vol. 2

Praca recenzowana / peer-reviewed pape

Initialization Mechanism in Kohonen Neural Network Implemented in CMOS Technology

An initialization mechanism is presented for Kohonen neural network implemented in CMOS technology. Proper selection of initial values of neurons’ weights has a large influence on speed of the learning algorithm and finally on the quantization error of the network, which for different initial parameters can vary even by several orders of magnitude. Experiments with the software model of designed network show that results can be additionally improved when conscience mechanism is used during the learning phase. This mechanism additionally decreases number of dead neurons, which minimizes the quantization error. The initialization mechanism together with experimental Kohonen neural network with four neurons and 3 inputs have been designed in CMOS 0.18 μm technology