Feature extraction from multi-axis MEMS sensors for unbalance detection in non-stationary rotating conditions

Conventional rotor balancers are driven by electric motors that maintain the machine rotor/shaft assembly at a constant rotational speed throughout the whole unbalance detection process. The present work expands the operating field of balancing machines, providing an algorithm enabling rotor balancing in non-stationary coasting-down regions, i.e., during the deceleration phase of the rotor/shaft assembly. In this context, the action of the electric motor, or of any other actuator, is just needed to bring the rotor/shaft assembly at the initial target rotational speed, reducing the overall energy consumption. Then, the actuator is deactivated and the unbalance is detected while the rotating mass is decelerating freely. The effectiveness of the proposed unbalance detection algorithm is proven over the case study of a human-powered wheel balancer. The proposed strategy allows the effective detection of static unbalanced masses higher than or equal to $20\mathrm{g}$ considering both ${15}^{\prime \prime }$ and ${16}^{\prime \prime }$ vehicle wheels.     

Wireless sensing for a solar power system

Wireless sensing is an excellent approach for remotely operated solar power system. Not only being able to get the sensor data, such as voltage, current, and temperature, the system can also have a proper control for tracking the Sun and sensing real-time data from a controller. In order to absorb the maximum energy by solar cells, it needs to track the Sun with proper angles. Arduino, H-bridge motor driver circuit, and Direct Current (DC) motor are used to alter the tilt angle of the solar PhotoVoltaic (PV) panel following the Sun while the azimuth and the elevation angles are fixed at noon. Unlike the traditional way, the tilt rotation is proposed to be stepped hourly. The solar PV panel is tilted ${7.5}^{\circ }$ in advance of current time to the west to produce more output voltage during an hour. As a result, the system is simple while providing good solar-tracking results and efficient power outputs.     

Work function difference of naphthyl end-capped oligothiophene in different crystal alignments studied by Kelvin probe force microscopy

The performance of organic semiconductor devices is strongly affected by the interface energetics at the junctions between the constituent materials. A large group of organic semiconductors consists of rodlike small molecules that crystallize upon deposition with a molecular orientation dependent on the specifics of the molecule–molecule and molecule–substrate interactions. By means of Kelvin probe force microscopy (KPFM), this work studies naphthyl end-capped oligothiophene, 5,50-bis(naphth-2-yl)-2,20-bithiophene (NaT2), deposited on samples of pristine ${\text{SiO}}_2$ and samples of graphene-covered ${\text{SiO}}_2$. The crystal molecular orientation of NaT2 is dependent on the substrate on which it is deposited. On ${\text{SiO}}_2$, the NaT2 molecules are predominately upright standing, forming crystallites with distinct terrace heights of $2.0\pm 0.1\text{nm}$. Measurements indicate formation of an initial wetting layer in the NaT2-${\text{SiO}}_2$ system for the upright standing molecules. When deposited on graphene, the molecules additionally form fibrous structures with heights of $10-115\text{nm}$ consisting of molecules lying down (face-on orientation). Using KPFM, a difference in the local contact potential difference (CPD) of upright standing NaT2 and face-on oriented structures on graphene is measured to be $-0.16\pm 0.04\text{V}$, indicating a work function difference between the two system configurations, which is confirmed through Density Functional Theory calculations.     

Bayesian sparse hierarchical model for image denoising

Sparse models and their variants have been extensively investigated, and have achieved great success in image denoising. Compared with recently proposed deep-learning-based methods, sparse models have several advantages: (1) Sparse models do not require a large number of pairs of noisy images and the corresponding clean images for training. (2) The performance of sparse models is less reliant on the training data, and the learned model can be easily generalized to natural images across different noise domains. In sparse models, ${\ell }_0$ norm penalty makes the problem highly non-convex, which is difficult to be solved. Instead, ${\ell }_1$ norm penalty is commonly adopted for convex relaxation, which is considered as the Laplacian prior from the Bayesian perspective. However, many previous works have revealed that ${\ell }_1$ norm regularization causes a biased estimation for the sparse code, especially for high-dimensional data, e.g., images. In this paper, instead of using the ${\ell }_1$ norm penalty, we employ an improper prior in the sparse model and formulate a hierarchical sparse model for image denoising. Compared with other competitive methods, experiment results show that our proposed method achieves a better generalization for images with different characteristics across various domains, and achieves state-of-the-art performance for image denoising on several benchmark datasets.     

Multitone reconstruction visual cryptography based on phase periodicity

The current no-computation grayscale image visual cryptography (VC) can only achieve halftone reconstruction but cannot truly achieve multitone. To solve this problem, we propose the concept of phase periodicity of the $\lambda /2$ retarder film and calculate the optical axis angle set with phase periodicity. According to the phase periodicity, we propose a $\lambda /2$ retarder film phase periodicity visual cryptography (RPP-VC). In RPP-VC, the secret pixels are encoded as the optical axis angles of $n$ $\lambda /2$ retarder films and distributed to $n$ shares. The decoding process does not require computation. The reconstructed image has no pixel expansion and can reach up to 23 tones. The quality of the reconstructed images has been greatly improved and the evaluation indicators of perceived quality are nearly doubled compared with other grayscale image VC schemes. The experimental results verify the feasibility of RPP-VC.