In this paper, a tri-band metamaterial (MTM) loaded compact monopole antenna is proposed. In the first step of design procedure, a rectangular monopole antenna is improved by replacing the corresponding rectangular patch with a ring resonator. As a result, the first obtained operating frequency is decreased from 2.95 to 2.46 GHz. Then, this operating frequency is reduced to 2.02 GHz utilizing an MTM geometry in the antenna structure. The geometry parameters of the proposed antenna are optimized to provide the applicability for 3G, WLAN, and WiMAX applications. The impedance bandwidths of 600, 1080, and 220 MHz are obtained at 2.02–2.62, 3.48–4.56, and 5.12–5.34 GHz, respectively. Moreover, the equivalent circuit of the proposed antenna has been extracted. The proposed equivalent circuit model is validated through a comparison with corresponding simulation results. The proposed antenna is compact, low profile, via-less, and provides easy fabrication. Considering the first resonance frequency, a compactness of 32% is achieved in comparison to the corresponding unloaded monopole antenna.

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Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.

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