Typical convolutional neural networks (CNNs) are widely used to recognize a user's stress state using the functional near-infrared spectroscopy (fNIRS), which is the latest brain imaging technology. fNIRS signals are usually fed into CNN models in the form of high-dimensional image data. However, this approach is not easy to achieve high classification accuracy because of physiological noises in brain signals. It is also likely to overlook the process of evaluating the reliability of calculated classification accuracy. To solve these problems, we proposed a memristor-based CNN (M-CNNs) This model's weight update process involves using stochastic gradient descent with momentum (SGDM), where the normalized conductances of memristors are used as weight substitutes. These conductances are then adjusted to classify stress states. We calculated the classification accuracies between the control and stress groups by using the M-CNNs, and then compared them with those of the CNNs. We used DenseNet, the most recent CNN model, to simulate accuracy under the same conditions. To ensure a fair comparison, we divided the DenseNet into the memristor-based DenseNet (M-DenseNet) and the conventional DenseNet (C-DenseNet). As a result, we discovered that the accuracy of M-CNNs (93.33%) exceeded that of CNNs (87.50%), and is reliable by precision, recall, and F-Score calculated from a confusion matrix. Likewise, M-DenseNet (92.38%) has higher accuracy than C-DenseNet (90.00%), but shows lower accuracy than M-CNNs. Moreover, we observed the reproducibility of M-CNN/DenseNet in various datasets. Therefore, our study suggests a promising application of CNN by combining conductances of memristor for classifying stress states.

We present a simple coplanar waveguide- (CPW-) fed rectangular ring monopole antenna designed for dual-band wireless local area network (WLAN) applications. The antenna is based on a simple structure composed of a CPW feed line and a rectangular ring. Dual-band WLAN operation can be achieved by controlling the distance between the rectangular ring and the ground plane of the CPW feed line, as well as the horizontal vertical lengths of the rectangular ring. Simulated and measured data show that the antenna has a compact size of21.4×59.4 mm², an impedance bandwidths of 2.21–2.70 GHz and 5.04–6.03 GHz, and a reflection coefficient of less than −10 dB. The antenna also exhibits an almost omnidirectional radiation pattern. This simple compact antenna with favorable frequency characteristics therefore is attractive for applications in dual-band WLAN.

A compact triple-band monopole antenna consisting of double rectangular rings and vertical slots cut into the ground is proposed for WLAN and WiMAX operations. The antenna has a compact size of 27.1 × 38.8 × 1.6 mm³, with simulated and measured impedance bandwidths of 2.37~2.81, 3.21~3.82, and 4.61~6.34 GHz with a reflection coefficient of less than −10 dB. The antenna also exhibits an almost omnidirectional radiation pattern and stable gain levels in the triple bands. The characteristics of the proposed antenna have been investigated using the numerical simulations and experiments.

A new coplanar waveguide bandstop filter using double hairpin-shaped defected ground structures is proposed. The proposed bandstop filter is composed of double hairpin-shaped defected ground structures etched on the ground plane of a coplanar waveguide, and can provide a narrow bandwidth rejection with a high Q factor. Numerical simulations revealed that the bandwidth and resonance frequency of this device can be controlled by adjusting the parameters of the defected ground structure. Moreover, numerical simulations and measurements revealed that a cascade of coplanar waveguides with double hairpin-shaped defected ground structures can increase the bandwidth, and attenuation level in the rejection band. Optimized cascaded coplanar waveguides loaded with double hairpin-shaped defected ground structures provide a Q factor of 21.1 with more than −40 dB suppression at 4.67 GHz. In many RF applications, this filter will be effective in rejecting unwanted signals with a high Q factor. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1265–1268, 2016

Recent advances in functional neuroimaging techniques, including methodologies such as fNIRS, have enabled the evaluation of inter-brain synchrony (IBS) induced by interpersonal interactions. However, the social interactions assumed in existing dyadic hyperscanning studies do not sufficiently emulate polyadic social interactions in the real world. Therefore, we devised an experimental paradigm that incorporates the Korean folk board game “Yut-nori” to reproduce social interactions that emulate social activities in the real world. We recruited 72 participants aged 25.2 ± 3.9 years (mean ± standard deviation) and divided them into 24 triads to play Yut-nori, following the standard or modified rules. The participants either competed against an opponent (standard rule) or cooperated with an opponent (modified rule) to achieve a goal efficiently. Three different fNIRS devices were employed to record cortical hemodynamic activations in the prefrontal cortex both individually and simultaneously. Wavelet transform coherence (WTC) analyses were performed to assess prefrontal IBS within a frequency range of 0.05–0.2 Hz. Consequently, we observed that cooperative interactions increased prefrontal IBS across overall frequency bands of interest. In addition, we also found that different purposes for cooperation generated different spectral characteristics of IBS depending on the frequency bands. Moreover, IBS in the frontopolar cortex (FPC) reflected the influence of verbal interactions. The findings of our study suggest that future hyperscanning studies should consider polyadic social interactions to reveal the properties of IBS in real-world interactions.