A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Mathematical Estimation of Logical Masking Capability of Majority/Minority Gates Used in Nanoelectronic Circuits

In nanoelectronic circuit synthesis, the majority gate and the inverter form the basic combinational logic primitives. This paper deduces the mathematical formulae to estimate the logical masking capability of majority gates, which are used extensively in nanoelectronic digital circuit synthesis. The mathematical formulae derived to evaluate the logical masking capability of majority gates holds well for minority gates, and a comparison with the logical masking capability of conventional gates such as NOT, AND/NAND, OR/NOR, and XOR/XNOR is provided. It is inferred from this research work that the logical masking capability of majority/minority gates is similar to that of XOR/XNOR gates, and with an increase of fan-in the logical masking capability of majority/minority gates also increases

ENHANCEMENT OF MARKOV RANDOM FIELD MECHANISM TO ACHIEVE FAULT-TOLERANCE IN NANOSCALE CIRCUIT DESIGN

As the MOSFET dimensions scale down towards nanoscale level, the reliability of circuits based on these devices decreases. Hence, designing reliable systems using these nano-devices is becoming challenging. Therefore, a mechanism has to be devised that can make the nanoscale systems perform reliably using unreliable circuit components. The solution is fault-tolerant circuit design. Markov Random Field (MRF) is an effective approach that achieves fault-tolerance in integrated circuit design. The previous research on this technique suffers from limitations at the design, simulation and implementation levels. As improvements, the MRF fault-tolerance rules have been validated for a practical circuit example. The simulation framework is extended from thermal to a combination of thermal and random telegraph signal (RTS) noise sources to provide a more rigorous noise environment for the simulation of circuits build on nanoscale technologies. Moreover, an architecture-level improvement has been proposed in the design of previous MRF gates. The redesigned MRF is termed as Improved-MRF. The CMOS, MRF and Improved-MRF designs were simulated under application of highly noisy inputs. On the basis of simulations conducted for several test circuits, it is found that Improved-MRF circuits are 400 whereas MRF circuits are only 10 times more noise-tolerant than the CMOS alternatives. The number of transistors, on the other hand increased from a factor of 9 to 15 from MRF to Improved-MRF respectively (as compared to the CMOS). Therefore, in order to provide a trade-off between reliability and the area overhead required for obtaining a fault-tolerant circuit, a novel parameter called as ‘Reliable Area Index’ (RAI) is introduced in this research work. The value of RAI exceeds around 1.3 and 40 times for MRF and Improved-MRF respectively as compared to CMOS design which makes Improved- MRF to be still 30 times more efficient circuit design than MRF in terms of maintaining a suitable trade-off between reliability and area-consumption of the circuit

Stronger computational modelling of signalling pathways using both continuous and discrete-state methods

Starting from a biochemical signalling pathway model expresses in a process algebra enriched with quantitative information, we automatically derive both continuous-space and discrete-space representations suitable for numerical evaluation. We compare results obtained using approximate stochastic simulation thereby exposing a flaw in the use of the differentiation procedure producing misleading results

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing