Binary signal transmission in nonlinear sensors: Stochastic resonance and human hand balance Many sensors exhibit nonlinear characteristics 1xe2x80x935 and are deployed in noisy environments 1xe2x80x937. In terms of device design and forming standards, this is a challenging area. However, it also presents opportunities for nonconventional signal processing methods based on stochastic resonance that have been shown to be of benefit for individual nonlinear sensors 1xe2x80x937, sensor arrays 3xe2x80x9310, sensor networks 3, 8, 11, and even portable devices for people with reduced sensory capacity 12xe2x80x9314. The most fascinating property of stochastic resonance is that nonlinear sensors connected in parallel or in a network yield improved performance over that achieved by using individual sensors 1xe2x80x9310. Studies in stochastic resonance have led to evidence of noiseenhanced signal transmission and processing in nonlinear sensors, and noise can be exploited in the design of engineered devices 2xe2x80x937, 10 and biological systems 1, 11xe2x80x9313. This paper studies noiseenhanced signal transmission and processing in nonlinear sensors and also exploits the positive role of noise in the design of engineered devices that enhance the sensitivity of hand movements.

Noiseinduced Performance Enhancement of Variabilityaware Memristor Networks Memristor networks are capable of lowpower, massive parallel processing and information storage. Moreover, they have widely used for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. However, till today, one of the major drawbacks resulting to their commercial cumbersome growth, is the fact that the fabricated memristor devices are subject to devicetodevice and cycletocycle variability that strongly affects the performance of the memristive network and restricts, in a sense, the utilisation of such systems for reallife demanding applications. In this work, we put effort on increasing the performance of memristive networks by incorporating external additive noise that will be proven to have a beneficial role for the memristor devices and networks. More specifically, we are taking inspiration from the wellknown nonlinear system phenomenon, called Stochastic Resonance, which alleges that noisy signals with specific characteristics can positively affect the operation of nonlinear devices. As such, we are now focusing on the utilisation of the phenomenon on memristor devices in a way that the negative effect of variability is reduced, thus the operation of the whole memristor network is assisted by the increased variability tolerance. The presented results of Bit Error Rate (BER) on a small ReRAM crossbar array sound promising and enable us to further investigate the exploitation of the described phenomenon by memristorbased networks and memories.