This paper presents gate level delay dependent probabilistic fault models for CMOS circuits operating at subthreshold and near-threshold supply voltages. A bottom-up approach has been employed: SPICE simulations have been used to derive higher level error models implemented using Verilog HDL. HSPICE Monte-Carlo simulations show that the delay dependent probabilistic nature of these faults is due to the process-voltage-temperature (PVT) variations which affect the circuits operating at very low supply voltages. For gate level error analysis, mutant based simulated fault injection (SFI) techniques have been employed for combinational netlist reliability analysis. Four types of gate level fault models, with different accuracies, are proposed. Our findings show that the proposed SFI method presents a 2X-5X simulation time overhead compared to the simulation of the gold circuit; with respect to SPICE analysis, the proposed method requires three orders of magnitude less simulation time.
Water is a vital source for life and natural environments. This is the reason why water sources should be constantly monitored in order to detect any pollutants that might jeopardize the quality of water. This paper presents a low-cost internet-of-things system that is capable of measuring and reporting the quality of different water sources. It comprises the following components: Arduino UNO board, Bluetooth module BT04, temperature sensor DS18B20, pH sensor—SEN0161, TDS sensor—SEN0244, turbidity sensor—SKU SEN0189. The system will be controlled and managed from a mobile application, which will monitor the actual status of water sources. We propose to monitor and evaluate the quality of water from five different water sources in a rural settlement. The results show that most of the water sources we have monitored are proper for consumption, with a single exception where the TDS values are not within proper limits, as they outperform the maximum accepted value of 500 ppm.
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