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The existing methods for multi-point harmonic contribution evaluation usually require some preconditions, such as the node harmonic current is equivalent to the harmonic emission current and the number of measurements is not less than the number of sources. However, these requirements may not be satisfied in practice. In this context, this paper proposes an improved sparse component analysis (SCA) method to solve the underdetermined blind source separation model, which is used to describe the harmonic state under incomplete measurements. The improved SCA adopts a new two-stage technique. First, the mixing matrix containing harmonic transfer impedances is estimated through the synchrosqueezing S-transform. Then, the source signal vector containing harmonic emission currents is reconstructed through the modified subspace projection. By matching the harmonic impedances and currents, the multi-point harmonic contribution evaluation is further achieved. The efficiency of the proposed method is verified by simulations and field case studies. The results demonstrate that the proposed method outperforms the existing methods in terms of accurately estimating the harmonic emission currents and quantifying the harmonic contributions under incomplete measurements. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dynamic key technique based on Cellular Neural Network (CNN) for security improvement in the orthogonal frequency division multiplexing passive optical network (OFDM-PON). To enhance the encryption scheme security, a six-dimensional CNN hyperchaotic system is employed to encrypt the data. And, the keys are divided into the dynamic and static. The dynamic key is randomly extracted from a key set by incorporating the random feature of the input data. Then, the chaotic sequence generated by the dynamic key is served as the synchronous sequence for encryption. Moreover, the chaotic sequences generated by the static keys are used to resist the chosen-plaintext attacks (CPAs) and scramble the phase of QAM symbols on the frequency domain. With these processing techniques, the multi-fold data encryption can create a key space of ∼10315 to protect against the exhaustive trial. The transmission of 10-Gb/s encrypted 16-QAM-based OFDM signal is demonstrated over 20-km single mode fiber (SMF) by experiment. The results show that our proposed scheme can provide excellent confidentiality of data transmission against the CPAs and brute-force attack. [ABSTRACT FROM AUTHOR]

This paper presents a 50 Gb/s per lane hybrid BiCMOS and silicon photonic integrated circuit for use in fiber optic communications. Fine pitch copper pillars are used to integrate electronics and silicon photonics. The resulting device demonstrates the generation and detection of up to 56 Gb/s NRZ optical signals over 2-km standard single-mode fiber at 1310-nm wavelength. At 40 Gb/s, the link operates error free, and at 56 Gb/s well below KR4 RS-FEC operating BER. The power dissipation of TX including CW laser is 600 mW (450-mW driver, 150-mW CW laser), RX is 150 mW, resulting in total per channel of less than 750 mW. [ABSTRACT FROM PUBLISHER]

Parkinson's disease (PD) is a neurodegenerative disease of the brain associated with motor symptoms. With the maturation of machine learning (ML), especially deep learning, ML has been used to assist in the diagnosis of PD. In this paper, we explore graph neural networks (GNNs) to implement PD prediction using MRI data. However, most existing GNN models suffer from the efficiency of graph construction on MRI data and the problem of overfitting on small data. This paper proposes a novel multi-layer GNN model that incorporates a fast graph construction method and a sparsity-based pooling layer with an attention mechanism. In addition, graph structure sparsity is plugged into the graph pooling layer as prior knowledge to mitigate overfitting in model training. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model and its superiority over baseline methods.