Automated Microfluidic Analysis of CUP-2 UOC for Forensic Applications

Poster contribution to the Defence and Security Doctoral Symposium 2023 EPSR

Fractal analysis of the effect of particle aggregation distribution on thermal conductivity of nanofluids

This project was supported by the National Natural Science Foundation of China (No. 41572116), the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan) (No. CUG160602).Peer reviewedPostprin

Spectrum-Efficient Triple-Layer Hybrid Optical OFDM for IM/DD-Based Optical Wireless Communications

In this paper, a triple-layer hybrid optical orthogonal frequency division multiplexing (THO-OFDM) for intensity modulation with direct detection (IM/DD) systems with a high spectral efficiency is proposed. We combine N-point asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM), N/2-point ACO-OFDM, and N/2-point pulse amplitude modulated discrete multitoned (PAM-DMT) in a single frame for simultaneous transmission. The time- and frequency-domain demodulation methods are introduced by fully exploiting the special structure of the proposed THO-OFDM. Theoretical analysis show that, the proposed THO-OFDM can reach the spectral efficiency limit of the conventional layered ACO-OFDM (LACO-OFDM). Simulation results demonstrate that, the time-domain receiver offers improved bit error rate (BER) performance compared with the frequency-domain with ∼40% reduced computation complexity when using 512 subcarriers. Furthermore, we show a 3 dB improvement in the peak-to-average power ratio (PAPR) compared with LACO-OFDM for the same three layers

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process