Power Splitting Based SWIPT in Network-Coded Two-Way Networks with Data Rate Fairness:An Information-Theoretic Perspective

This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting(EH) relay. We present a power splitting(PS)-based two-way relaying(PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signalto-noise ratio(SNR), the performance loss is relatively small. Another observation is that, in relatively high SNR regime, PS-TWR outperforms time switching-based two-way relaying(TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.

An Anti-Eavesdropping Method in Data Collection of Smart Meter

At present, DL/T 645-2007 communication protocol is used to collect data for smart meters. However, in the beginning, this protocol is not designed to be a secure protocol and only the function and reliability were taken into account. Plaintext is used in the protocol for data transmission, as a result, attackers can easily sniff the information and cause information leakage. In this paper, man-in-the-middle attack was used to verify that the smart meter data acquisition process was vulnerable when facing third-party attacks, and this can result in data eavesdropping. In order to resist such risks and prevent information being eavesdropped, a real ammeter communication experimental environment was built, it realized two-way identity authentication between data acquisition center and ammeter data center. At the same time, RSA (Rivest-Shamir-Adleman) was used to encrypt the meter data, which encrypted the collection, storage process of meter data and ensured the confidentiality and integrity of the meter data transmission. Compared with other methods, this method had obvious advantages. The analysis showed that this method can effectively prevent the data of smart meters from being eavesdropped.

Using Self-referencing Interlaced Submatrices to Determine the Number of Chemical Species in a Mixture

Determining the number of chemical species is the first step in analyses of a chemical or biological system. A novel method is proposed to address this issue by taking advantage of frequency differences between chemical information and noise. Two interlaced submatrices were obtained by downsampling an original data spectra matrix in an interlacing manner. The two interlaced submatrices contained similar chemical information but different noise levels. The number of relevant chemical species was determined through pairwise comparisons of principal components obtained by principal component analysis of the two interlaced submatrices. The proposed method, referred to as SRISM, uses two self-referencing interlaced submatrices to make the determination. SRISM was able to selectively distinguish relevant chemical species from various types of interference factors such as signal overlapping, minor components and noise in simulated datasets. Its performance was further validated using experimental datasets that contained high-levels of instrument aberrations, signal overlapping and collinearity. SRISM was also applied to infrared spectral data obtained from atmospheric monitoring. It has great potential for overcoming various types of interference factor. This method is mathematically rigorous, computationally efficient, and readily automated.

Basic Unit: As a Common Module of Neural Networks

In this paper,the logic is developed assuming that all parts of the brain are composed of a combination of modules that basically have the same structure.The feeding behavior of searching for food while avoiding the dangers of animals in the early stages of evolution is regarded as the basis of time series data processing.The module that performs the processing is presented by a neural network equipped with a learning function based on Hebb's rule,and is called a basic unit.The basic units are arranged in lay­ers,and the information between the layers is bidirectional.This new neural network is an extension of the traditional neural network that has evolved from pattern recognition.The biggest feature is that in the processing of time series data,the activated part changes according to the context struc­ture inherent in the data,and can be mathematically expressed the method of predicting events from the context of learned behavior and utilizing it in best action.