This manuscript provides a comprehensive tutorial on the operating principles of a bio-inspired Cellular Nonlinear Network, leveraging the local activity of NbO${}_{\mathrm{x}}$ memristors to apply a spike-based computing paradigm, which is expected to deliver such a separation between the steady-state phases of its capacitively-coupled oscillators, relative to a reference cell, as to unveal the classification of the nodes of the associated graphs into the least number of groups, according to the rules of a non-deterministic polynomial-hard combinatorial optimization problem, known as vertex coloring. Besides providing the theoretical foundations of the bio-inspired signal-processing paradigm, implemented by the proposed Memristor Oscillatory Network, and presenting pedagogical examples, illustrating how the phase dynamics of the memristive computing engine enables to solve the graph coloring problem, the paper further presents strategies to compensate for an imbalance in the number of couplings per oscillator, to counteract the intrinsic variability observed in the electrical behaviours of memristor samples from the same batch, and to prevent the impasse appearing when the array attains a steady-state corresponding to a local minimum of the optimization goal. The proposed Memristor Cellular Nonlinear Network, endowed with ad hoc circuitry for the implementation of these control strategies, is found to classify the vertices of a wide set of graphs in a number of color groups lower than the cardinality of the set of colors identified by traditional either software or hardware competitor systems. Given that, under nominal operating conditions, a biological system, such as the brain, is naturally capable to optimise energy consumption in problem-solving activities, the capability of locally-active memristor nanotechnologies to enable the circuit implementation of bio-inspired signal processing paradigms is expected to pave the way toward electronics with higher time and energy efficiency than state-of-the-art purely-CMOS hardware.

The memristor represents the key circuit element for the development of the constitutive blocks of future non-volatile memory architectures and neuromorphic systems. However, resistance switching memories offer a plethora of further opportunities for the electronics of the future. By virtue of the compatibility between the well-established CMOS technology and the fabrication process of most memristors, the exploitation of the peculiar dynamic behaviour of resistance switching memories, which, in general, differ depending upon their material composition, may allow the development of new circuits, which, processing information in unconventional forms, may extend and/or complement the functionalities of state-of-the-art electronic systems. Further, the attractive capability of real-world non-volatile memristors to store and process information in the same physical nanoscale location open the fascinating opportunity to improve the low throughput of Von Neumann computing machines, due to the limited bandwidth of the bus transferring data between the memory and the central processing unit. Finally, the extreme sensitivity of their electrical behaviour to small changes in their initial condition/input and the intrinsic stochastic variability in their switching dynamics may be harnessed to develop innovative bio-signal sensors as well as new cryptographic circuits and systems. The derivation of accurate mathematical models for the electrical behaviour of real-world memristor nano-devices, and their later circuit-and system-theoretic investigation aimed at drawing a comprehensive picture of their peculiar nonlinear dynamic behaviour under the set of inputs and initial conditions expected of the application of interest are fundamental steps towards their conscious future use in integrated circuit design. With this in mind, the present paper adopts a powerful theoretic tool known as Dynamic Route Map to analyse some of the most reliable physics-based models of real-world resistance switching memories to reveal how a particular dynamic phenomenon, known as fading memory, and recently discovered in a tantalum oxide non-volatile memristor device fabricated at Hewlett Packard Labs, is ubiquitous at nanoscale. The physical mechanisms behind the emergence of history-erase effects in non-volatile memristor nano-devices is explained thoroughly for both the DC and the AC periodic excitation scenarios.

Although laser-welding processes are frequently used in industrial production the quality control of these processes is not satisfactory yet. Until recently, the “full penetration hole” was presumed as an image feature which appears when the keyhole opens at the bottom of the work piece. Therefore it was used as an indicator for full penetration only. We used a novel camera based on “cellular neural networks” which enables measurements at frame rates up to 14 kHz. The results show that the occurrence of the full penetration hole can be described as a stochastic process. The probability to observe it increases near the full penetration state. In overlap joints, a very similar image feature appears when the penetration depth reaches the gap between the sheets. This stochastic process is exploited by a closed-loop system which controls penetration depth near the bottom of the work piece (“full penetration”) or near the gap in overlap joints (“partial penetration”). It guides the welding process at the minimum laser power necessary for the required penetration depth. As a result, defects like spatters are reduced considerably and the penetration depth becomes independent of process drifts such as feeding rate or pollution on protection glasses.

An intraoperative imaging system facilitating the localisation and characterisation of functional areas, pathological tissue, or perfusion disorders, could enormously support medical decisions during neurosurgical interventions and, thus, reduce the risk for the patients. To provide both structural and functional information of the brain tissue to the surgeon, a novel multimodal approach based on the measurement of long-wave infrared radiation and visual-light imaging is very promising. In this contribution, we discuss various methods for the registration and fusion of thermographic and visual-light images. The methods are evaluated quantitatively and qualitatively regarding their practicability during surgery. Furthermore, we introduce appropriate architectures for a digital hardware implementation of the registration and fusion algorithms. The designs are implemented on our reconfigurable intraoperative imaging system, revealing real-time processing performance.

Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas-insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophisticated analysis. In this paper, we present an analysis of a 1D-CNN-based approach for classifying laboratory PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS, under both negative and positive potentials. Most notably, our study demonstrates that this type of neural network, regardless of the training order, can generalize learnings to operating voltage multiples that it has not previously encountered. We evaluate this generalization performance under the presence of additional white Gaussian noise and investigate the influence of excluding the amplitude-related information in the signal. Further, we compare the network's performance when using input signals in both the time and frequency domain.

In this work the I–V characteristics of a niobium oxide-based threshold switching device were optimized to match the requirements for its application in neuromorphic circuits. Those neuromorphic circuits rely on coupled oscillators utilizing the volatile resistive switching effect of the memristor. A large voltage extension of the negative differential resistance region of the threshold switch enables enhanced signal amplification, and, furthermore, can lead to a better tolerance to device variability. A symmetric switching behavior as well as a high device stability for the operation in both voltage polarities is mandatory to allow the integration in circuits that utilize the connection of several threshold switching devices operated in different polarities. These properties are similarly important for the adoption of the threshold switches as selector devices in bipolar resistive memory arrays. Furthermore, a low forming voltage is desirable because it leads to a better control during the forming step. To meet all those requirements the application of multilayer stacks consisting of niobium and niobium oxide layers is proposed and their optimization is investigated in detail.