A delay-dependent LMI approach to dynamics analysis of discrete-time recurrent neural networks with time-varying delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the analysis problem for the existence and stability of periodic solutions is investigated for a class of general discrete-time recurrent neural networks with time-varying delays. For the neural networks under study, a generalized activation function is considered, and the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By employing the latest free-weighting matrix method, an appropriate Lyapunov–Krasovskii functional is constructed and several sufficient conditions are established to ensure the existence, uniqueness, and globally exponential stability of the periodic solution for the addressed neural network. The conditions are dependent on both the lower bound and upper bound of the time-varying time delays. Furthermore, the conditions are expressed in terms of the linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Two simulation examples are given to show the effectiveness and less conservatism of the proposed criteria.This work was supported in part by the National Natural Science Foundation of China under Grant 50608072, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany

Non-Hermitian coherent coupling of nanomagnets by exchange spin waves

Non-Hermitian physics has recently attracted much attention in optics and photonics. Less explored is non-Hermitian magnonics that provides opportunities to take advantage of the inevitable dissipation of magnons or spin waves in magnetic systems. Here we demonstrate non-Hermitian coherent coupling of two distant nanomagnets by fast spin waves with sub-50 nm wavelengths. Magnons in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave propagation. Our results are attractive for analog neuromorphic computing that requires unidirectional information transmission

On the predominant mechanisms active during the high power diode laser modification of the wettability characteristics of an SiO2/Al2O3-based ceramic material

The mechanisms responsible for modifications to the wettability characteristics of a SiO2/Al2O3-based ceramic material in terms of a test liquid set comprising of human blood, human blood plasma, glycerol and 4-octonol after high power diode laser (HPDL) treatment have been elucidated. Changes in the contact angle, , and hence the wettability characteristics of the SiO2/Al2O3-based ceramic were attributed primarily to: modifications to the surface roughness of the ceramic resulting from HPDL interaction which accordingly effected reductions in ; the increase in the surface O2 content of the ceramic after HPDL treatment; since an increase in surface O2 content intrinsically brings about a decrease in , and vice versa and the increase in the polar component of the surface energy, due to the HPDL induced surface melting and resolidification which consequently created a partially vitrified microstructure that was seen to augment the wetting action. However, the degree of influence exerted by each mechanism was found to differ markedly. Isolation of each of these mechanisms permitted the magnitude of their influence to be qualitatively determined. Surface energy, by way of microstructural changes, was found to be by far the most predominant element governing the wetting characteristics of the SiO2/Al2O3-based ceramic. To a much lesser extent, surface O2 content, by way of process gas, was also seen to influence to a changes in the wettability characteristics of the SiO2/Al2O3-based ceramic, whilst surface roughness was found to play a minor role in inducing changes in the wettability characteristics

Data quality: Some comments on the NASA software defect datasets

Background-Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets. In recent years, there has been much interest in using machine learners to classify software modules into defect-prone and not defect-prone categories. The publicly available NASA datasets have been extensively used as part of this research. Objective-This short note investigates the extent to which published analyses based on the NASA defect datasets are meaningful and comparable. Method-We analyze the five studies published in the IEEE Transactions on Software Engineering since 2007 that have utilized these datasets and compare the two versions of the datasets currently in use. Results-We find important differences between the two versions of the datasets, implausible values in one dataset and generally insufficient detail documented on dataset preprocessing. Conclusions-It is recommended that researchers 1) indicate the provenance of the datasets they use, 2) report any preprocessing in sufficient detail to enable meaningful replication, and 3) invest effort in understanding the data prior to applying machine learners