Advances in Design and Application of Spiking Neural Networks

This paper presents new findings in the design and application of biologically plausible neural networks based on spiking neuron models, which represent a more plausible model of real biological neurons where time is considered as an important feature for information encoding and processing in the brain. The design approach consists of an evolutionary strategy based supervised training algorithm, newly developed by the authors, and the use of different biologically plausible neuronal models. A dynamic synapse (DS) based neuron model, a biologically more detailed model, and the spike response model (SRM) are investigated in order to demonstrate the efficacy of the proposed approach and to further our understanding of the computing capabilities of the nervous system. Unlike the conventional synapse, represented as a static entity with a fixed weight, employed in conventional and SRM-based neural networks, a DS is weightless and its strength changes upon the arrival of incoming input spikes. Therefore its efficacy depends on the temporal structure of the impinging spike trains. In the proposed approach, the training of the network free parameters is achieved using an evolutionary strategy where, instead of binary encoding, real values are used to encode the static and DS parameters which underlie the learning process. The results show that spiking neural networks based on both types of synapse are capable of learning non-linearly separable data by means of spatio-temporal encoding. Furthermore, a comparison of the obtained performance with classical neural networks (multi-layer perceptrons) is presented.     

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator’s infrastructure. The obtained results are introduced and discussed.     

Habituation effects in computer security warning

Security warning is a form of computer dialog communication used to inform the users on the risks of allowing random applications to run on a computer system. Accordingly, it is specifically designed to impersonate a legitimate security alerting function (e.g., notify, warn, and advice) to a user about the consequence effect of an action. However, most of the computer users tend to ignore those security warnings conveying the same message over and over again. This eventually leads to habituation. Considering the fact that there is a significant lack of focus paid to address this issue, the main objective of this article is to describe and summarize the related studies on users’ habituation to the security warnings. This article presents a systematic literature review to explore the current key issues, challenges, and the possible solutions related to habituation effects in security warnings. It is expected that this article could contribute to a more complete understanding of the habituation effects in security warnings and eventually bring benefits to the research communities or general publics.     

Solving shortest path problem using particle swarm optimization

This paper presents the investigations on the application of particle swarm optimization (PSO) to solve shortest path (SP) routing problems. A modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-formation in the path construction process is proposed for particle representation in PSO. Simulation experiments have been carried out on different network topologies for networks consisting of 15–70 nodes. It is noted that the proposed PSO-based approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high certainty for all the tested networks. It is observed that the performance of the proposed algorithm surpasses those of recently reported genetic algorithm based approaches for this problem.     

Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults

In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.     

On the Convergence of a Greedy Rank-One Update Algorithm for a Class of Linear Systems

In this paper we study the convergence of the well-known Greedy Rank-One Update Algorithm. It is used to construct the rank-one series solution for full-rank linear systems. The existence of the rank one approximations is also not new, but surprisingly the focus there has been more on the applications side more that in the convergence analysis. Our main contribution is to prove the convergence of the algorithm and also we study the required rank one approximation in each step. We also give some numerical examples and describe its relationship with the Finite Element Method for High-Dimensional Partial Differential Equations based on the tensorial product of one-dimensional bases. We illustrate this situation taking as a model problem the multidimensional Poisson equation with homogeneous Dirichlet boundary condition.     

Obtaining a linear combination of the principal components of a matrix on quantum computers

Principal component analysis is a multivariate statistical method frequently used in science and engineering to reduce the dimension of a problem or extract the most significant features from a dataset. In this paper, using a similar notion to the quantum counting, we show how to apply the amplitude amplification together with the phase estimation algorithm to an operator in order to procure the eigenvectors of the operator associated to the eigenvalues defined in the range \(\left[ a, b\right] \), where a and b are real and \(0 \le a \le b \le 1\). This makes possible to obtain a combination of the eigenvectors associated with the largest eigenvalues and so can be used to do principal component analysis on quantum computers.     

Effect of the Te/Si ratio on the electrical characteristics of the amorphous chalcogenide switches

The effect of composition on amorphous chalcogenide threshold switches of the system Si _x Te_60-x As₃₀Ge₁₀, wherex = 5, 10, 12 and 20, has been studied. The compositionx = 5 shows the best switching characteristics, e.g. the smallest holding voltage (V _h = 0.4V), the highest ON state current (I _h = 45A) and smallest threshold voltage (V _s = 1.5V). Applying the three mechanisms of conductance of Mottet al. (Phil. Mag. 37 (1975) 961), it is found that for a particular composition ₂ < ₁ < ₀ (the pre-exponential factors) andW ₂ < (E _a –E ₁ +W ₁) < (E _c –E _f) (whereE _a,E _c andE _e = activation energies at band edge, fermi level and conduction band;W ₁ andW ₂ = activation energy for hopping). It was found that the density of states at the fermi levelN(E _f) increases with the decrease of silicon content. The results provided further evidence against thermal interpretations and thereby support electronic models of threshold switching for these glass systems.