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With the rapid development of 5G communication and wireless Internet of Things technology, the application of intelligent wearable devices based on wireless data transmission technology is becoming more and more popular. However, due to the bandwidth of wireless data transmission nodes and the power consumption of the device system, the use time and data storage of wearable devices are severely restricted. The compressed sensing (CS) technology has become an effective method to tackle this problem. CS technology includes data compressive sensing at the transmission end and data reconstruction at the receiving end. In this paper, we consider the reconstruction of nonnegative sparse vectors, an important problem in the area of CS for wireless data transmission. It is known that the interval-passing (IP) algorithm is a low-complexity message-passing type method for the problem. However, the reconstruction performance of the IP algorithm is inferior to that of the other state-of-the-art CS reconstruction algorithms such as the orthogonal matching pursuit (OMP) algorithm. In order to address the problem, we propose a two-stage reconstruction algorithm in this paper. The proposed algorithm applies the IP algorithm for the first stage of reconstruction. If the reconstruction fails, the OMP algorithm is then used on the basis of the results in the first stage. The proposed algorithm is evaluated and compared with other state-of-the-art algorithms by the probability of perfect reconstruction under the given sparsity order value. Simulation results suggest that the proposed two-stage algorithm can greatly improve the reconstruction performance of the IP algorithm and can even outperform the OMP algorithm. In addition, the low-complexity advantages of the IP algorithm are maintained in the proposed algorithm. [ABSTRACT FROM AUTHOR]

A new type of student information management system is designed to implement student information identification and management based on fingerprint identification. In order to ensure the security of data transmission, this paper proposes a data encryption method based on an improved AES algorithm. A new S-box is cleverly designed, which can significantly reduce the encryption time by improving ByteSub, ShiftRow, and MixColumn in the round transformation of the traditional AES algorithm with the process of look-up table. Experimental results show that the proposed algorithm can significantly improve the encryption time compared with the traditional AES algorithm. [ABSTRACT FROM AUTHOR]

VANET is a hot spot of intelligent transportation researches. For vehicle users, the file sharing and content distribution through roadside access points (AP) as well as the vehicular ad hoc networks (VANET) have been an important complement to that cellular network. So the AP deployment is one of the key issues to improve the communication performance of VANET. In this paper, an access point optimization method is proposed based on particle swarm optimization algorithm. The transmission performances of the routing protocol with randomlinear network coding before and after the access point optimization are analyzed. The simulation results show the optimization model greatly affects the VANET transmission performances based on network coding, and it can enhance the delivery rate by 25% and 14% and reduce the average delay of transmission by 38% and 33%. [ABSTRACT FROM AUTHOR]

For single-carrier transmission over delay-spread multi-input multi-output (MIMO) channels, the computational complexity of the receiver is often considered as a bottleneck with respect to (w.r.t.) practical implementations. Multi-antenna interference (MAI) together with intersymbol interference (ISI) provides fundamental challenges for efficient and reliable data detection. In this paper, we carry out a systematic study on the interference structure of MIMO-ISI channels, and sequentially deduce three different Gaussian approximations to simplify the calculation of the global likelihood function. Using factor graphs as a general framework and applying the Gaussian approximation, three low-complexity iterative detection algorithms are derived, and their performances are compared by means of Monte Carlo simulations. After a careful inspection of their merits and demerits, we propose a graph-based iterative Gaussian detector (GIGD) for severely delay-spread MIMO channels. The GIGD is characterized by a strictly linear computational complexity w.r.t. the effective channel memory length, the number of transmit antennas, and the number of receive antennas. When the channel has a sparse ISI structure, the complexity of the GIGD is strictly proportional to the number of nonzero channel taps. Finally, the GIGD provides a near-optimum performance in terms of the bit error rate (BER) for repetition encoded MIMO systems. [ABSTRACT FROM AUTHOR]