Hybrid RFF Identification for LTE Using Wavelet Coefficient Graph and Differential Spectrum

The growing popularity of 4 G/5 G mobile devices has led to an increase in demand for wireless security. Radio frequency fingerprint (RFF) technique is an emerging approach for device authentication using intrinsic and unique hardware impairments. In this paper, we propose an RFF-based method to identify rogue/unknown long term evolution (LTE) terminals. This is achieved by combining wavelet transform (WT) coefficient graphs and differential spectrum. The proposed method involves extracting 48 levels of wavelet coefficients from the transient power-off of the physical random access channel (PRACH) signal and representing them in a WT graph. The steady-state part of the PRACH signal after a frequency domain differential processing between the adjacent spectrum is extracted. To detect unknown attack devices, an identification scheme based on an autoencoder (AE) is designed. Two different AE network structures are designed based on the proposed features, and a hybrid identification structure is proposed. An experimental evaluation system is set up with seven mobile phones from three categories and one universal software radio peripheral (USRP) software-defined radio (SDR) platform. Training and testing datasets are collected under different conditions such as location, working times, and dates. Experimental results show that rogue devices can be identified with an accuracy up to 98.84% for different categories and 90.27% for different individuals

Authorized and Rogue LTE Terminal Identification Using Wavelet Coefficient Graph with Auto-encoder

The wide popularity of 4G/5G mobile terminals increase the requirements of wireless security. Radio frequency fingerprint (RFF) technology can strengthen 4G/5G air interface accessing security at the physical layer. In this paper, a wavelet transform (WT) coefficient graphs RFF extraction with auto-encoder (AE) based rogue terminal detection scheme is proposed. At first, WT coefficients at 48 scales are extracted from the transient-power-off part of LTE physical random access channel (PRACH) preamble. Then, an AE network structure aimed for 2D WT coefficient graph is designed for rogue terminal detection. We successfully distinguish 7 mobile phones and 1 USRP under the proposed mechanism, where the authorized terminals from the same manufacturer can be identified with an accuracy of 90.08%. In addition, extensive experiments are carried out at LOS and NOLS scenarios, respectively, the proposed LTE identification scheme has demonstrated robustness in dynamic environments

Signal-independent RFF Identification for LTE Mobile Devices via Ensemble Deep Learning

Radio frequency fingerprint (RFF)-based wireless device authentication is an emerging technique to prevent potential spoofing attacks in wireless communications. The random access preamble of the physical random access channel (PRACH) in Long Term Evolution (LTE) systems is the first message sent from a user equipment (UE). However, PRACH preambles change under different evolved Node B (eNB), which will affect the RFF extraction. In this paper, a signal-independent RFF extraction method is first proposed to extract varying LTE PRACH preambles under different LTE eNBs. Residual transient segment (RTS) features from the varying PRACH preambles are extracted for RFF identification. A convolutional neural network (CNN) based ensemble deep learning scheme is proposed to integrate benefits from different RFF features. An experimental system under real operator LTE eNB is designed to capture and identify real UE signals. Experimental results show that the classification accuracy of five UEs can reach more than 95% under the same eNB and 85% under different eNBs. Furthermore, longtime evaluations show that the UE RTS feature is robust over time

Design of Noise Robust Open-Set Radio Frequency Fingerprint Identification Method

Radio frequency fingerprint (RFF) identification (RFFI) is a promising technique for device authentication at the physical layer of the communication stacks. However, practical challenges, particularly in low signal-to-noise ratio (SNR) scenarios, and the lack of comprehensive studies on open-set recognition hinder the widespread application of RFFI. This paper presents an unsupervised open-set RFF identification algorithm designed to address the robustness challenges associated with low SNR. Our approach integrates the Noise2Noise method for denoising, drawing inspiration from its successful applications in image and speech processing. The proposed framework utilizes an image- based autoencoder (AE) to extract features from the differential constellation trace figure (DCTF) of the signals after Noise2Noise denoising. The open-set recognition task is performed by cosine distance measurement. We carried out extensive experimental evaluation involving 18 ZigBee devices and a USRP software-defined radio platform. Our proposed method can achieve a gain up to 25% under low SNR

Prediction of body fat increase from food addiction scale in school-aged children and adolescents: A longitudinal cross-lagged study

ObjectiveFood addiction (FA) is associated with a higher body mass index z-score (BMIZ) in children and adolescents; however, whether these two aspects evolve interdependently remains unknown. This study aimed to address this question using a cross-lagged study.MethodsWeight status, including BMIZ, fat content (FC), and visceral fat level (VFL), was determined in 880 children and adolescents (mean age = 14.02 years [range = 8.83–17.52 years]) at two-time points with an interval of 6 months. FA was characterized using the Chinese version of the dimensional Yale Food Addiction Scale for Children 2.0. Furthermore, FC and VFL were measured using direct segmental multi-frequency bioelectrical impedance analysis at each time point.ResultsHigher FA was associated with increased BMIZ, FC, and VFL (P < 0.05). FA at T0 could predict increased FC at T1 (P < 0.05). The characteristics of females, primary students, and living in urban areas may aggravate the adverse effect of FA on weight status over time and age, particularly the increased VFL in participants aged > 14 years.ConclusionChildren and adolescents with a high FA level were at risk for weight gain attributed to increased FC, and the adverse effect could be aggravated with time and age. Novel FA-targeting interventions may help mitigate the risk of getting obesity

Multi-Channel CNN-Based Open-Set RF Fingerprint Identification for LTE Devices

Radio frequency fingerprint identification (RFFI) is a promising technique that exploits the transmitter-specific characteristics of the RF chain for identification. Disregarding its massive deployment, long-term evolution (LTE) systems have not fully benefited from RFFI. In this paper, an RFFI technique is designed to authenticate LTE devices. Three segments of the LTE physical layer random access channel (PRACH) preambles are captured, namely the transient-on, transient-off, and modulation parts. The segments are first converted into differential constellation trace figures (DCTFs), and then a specific type of neural network called multi-channel convolutional neural network (MCCNN) is used for identification. Additionally, the protocol is able to be applied for open-set identification, i.e., unknown device detection. Experiments are conducted with ten LTE mobile phones. The results show that the proposed RFFI scheme is robust against location changes. In the known device classification problem, the classification accuracy can reach 98.70% in the line-of-sight (LOS) scenario and 89.40% in the non-line-of-sight (NLOS) scenario. In the open-set unknown device detection problem, the identification equal error rate (EER) and area under the curve (AUC) reach 0.0545 and 0.9817, respectively, among six known devices and four unknown devices

Fluorescent and magnetic dual-responsive coreshell imprinting microspheres strategy for recognition and detection of phycocyanin

Molecular imprinting as a versatile technology is emerging for diverse species in various fields; however protein imprinting faces several problems related to the size, structural complexity, conformational flexibility, and compatibility with solvents. Herein, by using phycocyanin as a model, with physiological significance and fluorescence characteristics, we developed a facile and highly efficient approach to obtain fluorescent and magnetic dual-responsive coreshell imprinting microspheres. Twostage miniemulsion polymerization was employed, based on surface immobilization of phycocyanin with aminolysis and aldehyde modification on superparamagnetic support particles. The dual-responsive imprinting microspheres exhibited high adsorption capacity of 10.53 mg g(-1), excellent binding selectivity toward phycocyanin with a high imprinting factor of 2.41, and good reproducibility with standard error within 10%. Furthermore, fast simple magnetic separation and sensitive fluorescent detection in a wide pH range was offered for phycocyanin, showing a good linearity within 0.01-1.0 mg L-1 (R-2 = 0.9970) and a favorable detectability up to 1.5 ng mL(-1). Consequently, the imprinting microspheres were successfully applied as sorbents for selective isolation of phycocyanin from protein mixtures and special imaging recognition. Taking advantages of dual-responsive polymers and surface imprinting, the developed strategy provides great application potentials for convenient, rapid targeting identification/enrichment and separation of proteins and thereby contributing to targeting drug delivery and protein research.Molecular imprinting as a versatile technology is emerging for diverse species in various fields; however protein imprinting faces several problems related to the size, structural complexity, conformational flexibility, and compatibility with solvents. Herein, by using phycocyanin as a model, with physiological significance and fluorescence characteristics, we developed a facile and highly efficient approach to obtain fluorescent and magnetic dual-responsive coreshell imprinting microspheres. Twostage miniemulsion polymerization was employed, based on surface immobilization of phycocyanin with aminolysis and aldehyde modification on superparamagnetic support particles. The dual-responsive imprinting microspheres exhibited high adsorption capacity of 10.53 mg g(-1), excellent binding selectivity toward phycocyanin with a high imprinting factor of 2.41, and good reproducibility with standard error within 10%. Furthermore, fast simple magnetic separation and sensitive fluorescent detection in a wide pH range was offered for phycocyanin, showing a good linearity within 0.01-1.0 mg L-1 (R-2 = 0.9970) and a favorable detectability up to 1.5 ng mL(-1). Consequently, the imprinting microspheres were successfully applied as sorbents for selective isolation of phycocyanin from protein mixtures and special imaging recognition. Taking advantages of dual-responsive polymers and surface imprinting, the developed strategy provides great application potentials for convenient, rapid targeting identification/enrichment and separation of proteins and thereby contributing to targeting drug delivery and protein research

The relationships among self-efficacy, achievement motivation, and work values for regular four-year university students and community college students in China

This study investigated the relationships among self-efficacy, achievement motivation, and work values for both regular four-year university students and community college students in China. Self-efficacy and achievement motivation were independent variables measured by Wang’s (1999) Self-Efficacy Inventory (SEI) and Ye and Hagtvet’s (1992) Achievement Motivation Scale (AMS), respectively. The dependent variable was work values measured by Jin and Li’s (2005) Work Values Scale (WVS). A total of 384 students participated in this study, 186 from a regular four-year university and 198 from a community college in Beijing, China. For the comparison between the two types of institutions, results showed that community college students had lower levels of self-efficacy, higher levels of motivation to avoid failure, and lower intentional work values than regular four-year university students. For the relationship among the three variables, results indicated that: (1) Students with higher levels of self-efficacy focused more on intentional work values including family, status, achievement, and social improvement; (2) Students with higher levels of motivation to achieve success focused more on intentional work values and students with higher levels of motivation to avoid failure focused more on instrumental work values; (3) Students with higher levels of self-efficacy had higher levels of overall achievement motivation and motivation to achieve success but lower levels of motivation to avoid failure; and (4) Institution type had a mediation effect in the relationship between self-efficacy and the first factor of the instrumental work values, stability. This study was an attempt to focus on community college students as well as regular four-year university students. Detailed results and implications to career development of college students, especially community college students, in the China’s background of economic development were discussed

Determination of Heavy Metals in Alpinia oxyphylla Miq. Collected from Different Cultivation Regions

20 batches of Alpinia oxyphylla Miq. were collected from Yunnan, Guangdong, Guangxi, and Hainan province in China. The contents of heavy metals of As, Hg, Pb, Cd, and Cu were determined and compared. The results indicated that geographical source might be a major factor to influence the contents of heavy metals of arsenic (As), mercury (Hg), lead (Pb), cadmium (Cd), and copper (Cu) in Alpinia oxyphylla Miq. Compared to the criteria of heavy metals, the contents of As, Hg, Pb, and Cd in almost all the samples were in accordance with The Green Trade Standards. The contents of Cu were higher than the criteria for heavy metals except the samples from Changxing town, Qiongzhong county, Maoyang town, Qiongzhong county, Wupo town, Tunchang county, and Nanlv town, Tunchang county, in Hainan province. The best cultivation regions of Alpinia oxyphylla Miq. were from Changxing town, Qiongzhong county, Maoyang town, Qiongzhong county, Wupo town, Tunchang county, and Nanlv town, Tunchang county, in Hainan province. This research would provide the scientific basis for quality control and standardization of Alpinia oxyphylla Miq