The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 min. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.

The peer-to-peer (P2P) network of blockchain used to transport its transactions and blocks has a high impact on the efficiency and security of the system. The P2P network topologies of popular blockchains such as Bitcoin and Ethereum, therefore, deserve our highest attention. The current Ethereum blockchain explorers (e.g., Etherscan) focus on the tracking of block and transaction records but omit the characterization of the underlying P2P network. This work presents the Ethereum Network Analyzer (Ethna), a tool that probes and analyzes the P2P network of the Ethereum blockchain. Unlike Bitcoin that adopts an unstructured P2P network, Ethereum relies on the Kademlia DHT to manage its P2P network. Therefore, the existing analytical methods for Bitcoin-like P2P networks are not applicable to Ethereum. Ethna implements a novel method that accurately measures the degrees of Ethereum nodes. Furthermore, it incorporates an algorithm that derives the latency metrics of message propagation in the Ethereum P2P network. We ran Ethna on the Ethereum Mainnet and conducted extensive experiments to analyze the topological features of its P2P network. Our analysis shows that the Ethereum P2P network possesses a certain effect of small-world networks, and the degrees of nodes follow a power-law distribution that characterizes scale-free networks.

Occlusion poses a major challenge for person re-identification (ReID). Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy. In particular, they may fail when facing complex occlusions, such as those between pedestrians. Accordingly, in this paper, we propose a novel method named Quality-aware Part Models (QPM) for occlusion-robust ReID. First, we propose to jointly learn part features and predict part quality scores. As no quality annotation is available, we introduce a strategy that automatically assigns low scores to occluded body parts, thereby weakening the impact of occluded body parts on ReID results. Second, based on the predicted part quality scores, we propose a novel identity-aware spatial attention (ISA) module. In this module, a coarse identity-aware feature is utilized to highlight pixels of the target pedestrian, so as to handle the occlusion between pedestrians. Third, we design an adaptive and efficient approach for generating global features from common nonoccluded regions with respect to each image pair. This design is crucial, but is often ignored by existing methods. QPM has three key advantages: 1) it does not rely on any outside tools in either the training or inference stages; 2) it handles occlusions caused by both objects and other pedestrians; 3) it is highly computationally efficient. Experimental results on four popular databases for occluded ReID demonstrate that QPM consistently outperforms state-of-the-art methods by significant margins. The code of QPM will be released.

Authentication is a key requirement for secure communications in modern wireless systems. Compared with the conventional authentication at the upper layer using a cryptographic tool, authentication at the physical layer has many advantages, including enhanced security through the introduction of uncertainty to adversaries and increased efficiency and compatibility through the avoidance of operations at the upper layer, particularly in heterogeneous coexistence environments, e.g., 5G wireless systems. In this paper, we investigate authentication at the physical layer under time-varying fading channels. Conventional authentication schemes at the physical layer operate poorly under fast fading or frequency selective fading channels. Furthermore, conventional schemes require additional complicated preprocessing such as channel estimation and message symbol recovery through demodulation and decoding. This paper proposes a new blind authentication scheme at the physical layer that combines the techniques of blind known interference cancellation (BKIC) and differential processing to implement authentication without requiring any of the above-described preprocessing. The proposed scheme utilizes both the smoothing (SM) technique and belief propagation (BP) technique to achieve BKIC through the distinct blind authentication schemes with a superimposed tag over pilots (BSUPs) referred to, respectively, as BSUP-SM and BSUP-BP. The proposed scheme not only effectively suppresses the deteriorate effect of fading channels without any additional preprocessing but is also covert to unaware users, robust to interference, and secure for identity verification. The tradeoffs of using the proposed system with respect to various goals are discussed and analyzed. The performance of the BSUP-SM scheme depends on the channel fading and the length of the pilot cluster, while the performance of the BSUP-BP scheme is not sensitive to the above factors but depends instead on the quantization step used. The BSUP-BP scheme works well in fast fading channels, even in frequency selective fading channels.