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Quantum electronic devices with negative differential resistance (NDR) characteristics have been used to design compact multiplexers. These multiplexers may be used either as analog multiplexers where the signal on a single select line selects one out of four analog inputs, or as four-valued logic multiplexers where the select line and the input lines represent one of four quantized signal values and the output line corresponds to the selected input. Any four-valued logic function can be implemented using only four-valued multiplexers (also known as T-gates), and this T-gate uses just 13 devices (transistors) as compared to 44 devices in CMOS. The design of the T-gate was done using a combination of resonant tunneling diodes (RTD's) and heterojunction bipolar transistors (HBT's) with the folded I-V characteristic (NDR characteristic) of the RTD's providing the compact logic implementation and the HBT's providing the gain and isolation. The application of the same design principles to the design of T-gates using other NDR devices such as resonant tunneling hot electron transistors (RHET's) and resonant tunneling bipolar transistors (RTBT's) is also demonstrated. .

Water distribution network is a costly infrastructure and plays a crucial role in supplying water for the consumers especially for those who are living in the urban areas. The importance and huge capital cost of the system leads to considerable attention on seeking the optimal cost design. The necessity for such a sound research attention arises from the complexity associated with the problem. In the recent years, stochastic optimization algorithms like genetic algorithm, simulated annealing, ant colony optimization etc. are found to be successful in exploring the optimal combination of pipe diameters that can satisfy the hydraulic-head requirements with least cost. In this paper, the details on the optimal water distribution network design with a novel technique called honey-bee mating optimization and its validation with two benchmark water distribution networks are presented. From the results, it is observed that the proposed algorithm identifies the optimal solution with relatively less number of evaluations than the other well-established stochastic optimization algorithms.