Changyi Yang

The microstructural stability and property evolution of high-temperature titanium alloys under long-term high-temperature conditions has been a critical scientific issue in the field of advanced titanium alloys. In this work, we systematically investigated the precipitation behavior of silicides and ordered α₂ phase, which are closely related to the microstructural stability of Ti65 high-temperature alloy, during thermal exposure at 650 °C for different periods of time. Furthermore, the effects of thermal exposure on mechanical properties were evaluated using room temperature and high temperature tensile tests, and subsequently, the correlation between the microstructural thermal stability and the mechanical characteristics was discussed. The results reveal that (Ti, Zr)₆Si₃ silicides initially precipitate within the residual β film and then start to precipitate in the α platelet. A large number of fine spherical α₂ precipitates were formed inside the α platelet after a short thermal exposure. The number density of ordered α₂ decreased significantly after 1000 h due to Ostwald ripening. The precipitation of silicides and ordered α₂ phases during thermal exposure improves the tensile strength but deteriorates the ductility, and the room-temperature ductility is slightly restored due to α₂ ripening after long-time thermal exposure. Ti65 high-temperature titanium alloy consistently maintains favorable room-temperature tensile properties throughout long-term thermal exposure.

Direction of arrival (DOA) is becoming more and more important for underwater transportation security especially in shallow ocean. In this paper, a DOA estimation technique for underwater transportation via Compressed Sensing (CS) based on Vector Sensor Array (VSA) is proposed. Different from most of the pressure sensor arrays, VSA can make fully use of the available acoustic information (pressure and particle velocity) and obtain better estimation performance. In addition, the DOA estimation problem in underwater transportation is converted into a sparsity representation problem and solved by the convex optimization toolbox CVX. Computer simulations show that our proposed method can outperform other traditional methods.

As technology advances, many countries have now opted for electronic voting systems. Any voting system must follow principles of transparency and impartiality in order to achieve fairness; the electronic voting process must also be protected against cyberattacks or denial-of-service attacks (DDOS) because such attacks may affect the processing time in voting procedures and even hinder the fairness in voting. This study establishes a network security mechanism for voting systems based on blockchain technology. The blockchain mechanism employs a distributed architecture that can prevent system shutdown resulting from malicious cyberattacks; additionally, any user in the blockchain can authenticate data integrity, which satisfies requirements of transparency and impartiality in voting systems. This study utilizes bilinear pairing to establish network security in voting systems, which call for anonymity, authenticity, integrity, and non-repudiation. When authenticating voting integrity, the user's anonymity must be ensured to prevent identity revelation, and the data must be protected against malicious tampering such security measures also fulfill blockchain requirements. The proposed voting system relies on the basis of blockchains to create a trustworthy voting system. In current blockchain technology, smart contracts allow the establishment of voter-related regulations to prevent controversies during voting processes. Additionally, in order to establish both a secret ballot and an open ballot system, the study also implements a bilinear pairing security mechanism to ensure the overall security of a voting procedure.

The issues of carrier frequency offset (CFO) and channel estimation are very important for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless communication systems. In this paper, the problems of CFO and channel estimation are considered. An adaptive estimation algorithm is proposed. It can estimate CFO and channel parameters at the same time. Since the estimation method is real-time, it can work at a fast time-varying environment. However, the model becomes a huge nonlinear system if CFO and channel parameters are estimated simultaneously. It is a heavy load for the wireless communication system, even the extended Kalman filter can solve the nonlinear estimation. A multiple model, which includes many subchannel models, is constructed. The subchannel with CFO can be separately estimated. The accurate CFO is obtained by integrating estimated CFOs from subchannel models with weighting technology. The computational complexity can be reduced by this method. Finally, several simulations are given to prove the performance in comparison with other methods.

This paper presents a 12-bit 270MS/s pipelined analog-to-digital converter (ADC) without employing a front-end sample-and-hold amplifier. A novel strategy is established to diminish the aperture error while maintaining both the original tracking time and amplifying time of multiplying digital-to-analog converter (MDAC). It matches the signal paths between comparators and MDAC in the first stage by using proper timing sequence and high-speed dynamic comparators. The measurement results show 63.7dB SNR and 76.1dBc SFDR at 120.1MHz input frequency while the chip's total power dissipation is 250mW (excluding LVDS drivers) at 1.2V supply. The ADC core occupies 1.7mm ² and is implemented in a 130nm CMOS process.