ABSTRACT
It has long been a challenge to obtain high spectral and spatial resolution simultaneously for the field of measurement and detection. Here we present a measurement system based on single-pixel imaging with compressive sensing that can realize excellent spectral and spatial resolution at the same time, as well as data compression. Our method can achieve high spectral and spatial resolution, which is different from the mutually restrictive relationship between the two in traditional imaging. In our experiments, 301 spectral channels are obtained in the band of 420-780 nm with a spectral resolution of 1.2 nm and a spatial resolution of 1.11 mrad. A sampling rate of 12.5% for a 64×64p i x e l image is obtained by using compressive sensing, which also reduces the measurement time; thus, high spectral and spatial resolution are realized simultaneously, even at a low sampling rate.
ABSTRACT
Image-based target tracking methods rely on continuous image acquisition and post-processing, which will result in low tracking efficiency. To realize real-time tracking of fast moving objects, we propose an image-free target tracking scheme based on the discrete cosine transform and single-pixel detection. Our method avoids calculating all the phase values, so the number of samples can be greatly reduced. Furthermore, complementary modulation is applied to reduce the measurement noise, and background subtraction is applied to enhance the contrast. The results of simulations and experiments demonstrate that the proposed scheme can accomplish the tracking task in a complex background with a sampling ratio of less than 0.59% of the Nyquist-Shannon criterion, thereby significantly reducing the measurement time. The tracking speed can reach 208 fps at a spatial resolution of 128 × 128 pixels with a tracking error of no more than one pixel. This technique provides a new idea for real-time tracking of fast-moving targets.
ABSTRACT
An interpolation computational ghost imaging (ICGI) method is proposed and demonstrated that is able to reduce the noise interference from a fluctuating source and background. The noise is estimated through periodic illuminations by a specific assay pattern during sampling, which is then used to correct the bucket detector signal. To validate this method simulations and experiments were conducted. Light source intensity and background lighting were randomly varied to modulate the noise. The results show that good quality images can be obtained, while with conventional computational ghost imaging (CGI) the reconstructed object is barely recognizable. The ICGI method offers a general approach applicable to all CGI techniques, which can attenuate the interference from source fluctuations, background light noise, dynamic scattering, and so on.
ABSTRACT
Two-dimensional gyroscopic torque can be produced by tilting the rotor shaft of the active magnetically suspended momentum wheel. The nonlinear magnetic torque is analyzed and then an adaptive back-stepping tracking method is proposed to deal with the nonlinearity and uncertainty. The nonlinearity of magnetic torque is represented as bounded unknown uncertainty stiffness, and an adaptive law is proposed to estimate the stiffness. Combined with back-stepping method, the proposed method can deal with the uncertainty. This method is designed by Lyapunov stability theory to ensure the stability, and its effectiveness is validated by simulations and experiments. These results indicate that this method can realize higher tracking precision and faster tracking velocity than the conventional cross feedback method to provide high precision and wide bandwidth outputting torque.
