In this paper, a compact ultra-wideband receiving antenna is proposed. It consists of a top loaded planar monopole and a circular ground plane. This design combines various techniques such as the comb shaped top loading and the integrated lumped elements to effectively reduce the antenna size, while keeping a wide bandwidth and a reasonable realized gain. The proposed antenna operates across a wide frequency range from 20 to 8 GHz (for S 11 <À6 dB), occupies a compact volume of 500 Â 500 Â 340 mm 3 (0.033λ 0 Â 0.033 λ 0 Â 0.023 λ 0 at 20 MHz) and has a stable omnidirectional radiation pattern in azimuth plane from 20 to 6 GHz. Simulations and measurements are conducted to validate the antenna performance. It is demonstrated that this antenna is well suited to a variety of wideband radio frequency receivers for monitoring use.
Multi-label text classification (MLTC) addresses a fundamental problem in natural language processing, which assigns multiple relevant labels to each document. In recent years, Neural Network-based models (NN models) for MLTC have attracted much attention. In addition, NN models achieve favorable performances because they can exploit label correlations in the penultimate layer. To further capture and explore label correlations, we propose a novel initialization to incorporate label co-occurrence into NN models. First, we represent each class as a column vector of the weight matrix in the penultimate layer, which we name the class embedding matrix. Second, we deduce an equation for correlating the class embedding matrix with the label co-occurrence matrix, ensuring that relevant classes are denoted by vectors with large correlations. Finally, we provide a theoretical analysis of the equation, and propose an algorithm to calculate the initial values of the class embedding matrix from the label co-occurrence matrix. We evaluate our approach with various text extractors, such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Transformer on four public datasets. The experimental results demonstrate that our approach markedly improves the performance of existing NN models.INDEX TERMS Multi-label text classification, label co-occurrence, initialization, neural network, class embedding.
Coping with the problem of malicious third-party vendors implanting Hardware Trojan (HT) in the circuit design stage, this paper proposes a hybrid-mode gate-level hardware Trojan detection platform based on the XGBoost algorithm. This detection platform is composed of multi-level HT localization and circuit structure based HT detection. Each wire of the circuit is regarded as a node in multi-level HT localization, and static characteristics of nodes are analysed, combining with dynamic detection to locate HT. The network structure features of the circuit are extracted in modular HT structure detection, aiming to identify HT accurately and rapidly. The hybrid-mode HT detection platform can efficiently meet various detection requirements, such as HT localization or rapid and accurate HT detection. The experiment results on Trust-Hub benchmark show that the multi-level localization can achieve 94.0% location accuracy, and the modular HT structure detection accuracy can achieve 100%. The modular HT structure detection is about four times as fast as the multi-level HT localization on feature extraction. Therefore, multi-level localization and modular HT structure detection can be respectively or cooperatively applied for specific HT detection issues, which proves that the proposed hybrid-mode gate-level HT detection scheme is practical and effective.
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