Development of Sliding Mode Controller Based on Internal Model Controller for Higher Precision Electro-Optical Tracking System

The electro-optical tracking system (ETS) on moving platforms is affected by the vibration of the moving carrier, the wind resistance torque in motion, the uncertainty of mechanisms and the nonlinear friction between frames and other disturbances, which may lead to the instability of the electro-optical tracking platform. Sliding mode control (SMC) has strong robustness to system disturbances and unknown dynamic external signals, which can enhance the disturbance suppression ability of ETSs. However, the strong robustness of SMC requires greater switching gain, which causes serious chattering. At the same time, the tracking accuracy of SMC has room for further improvement. Therefore, in order to solve the chattering problem of SMC and improve the tracking accuracy of SMC, an SMC controller based on internal model control (IMC) is proposed. Compared with traditional SMC, the proposed method can be used to suppress the strongest disturbance with the smallest switching gain, effectively solving the chattering problem of the SMC, while improving the tracking accuracy of the system. In addition, to reduce the adverse influence of sensor noise on the control effect, lifting wavelet threshold de-noising is introduced into the control structure to further improve the tracking accuracy of the system. The simulation and experimental results verify the superiority of the proposed control method.

Elasticity and damping ratio measurement of droplets on super-hydrophobic surfaces

The measurement of the droplets’ elasticity is vitally important in microfluidic and ink-jet printing. It refers to the ability of the droplet to restore its original shape and strong robustness. This study investigated a novel method to measure elasticity. The plate coated with super-hydrophobic layers pressed on a droplet and the elastic force was recorded by an electronic balance. Meanwhile, a mathematical model was constructed to calculate the changes of the droplet area under the force. The measurement showed that external work mainly converts into surface energy and the damping ratio increases from 0.068 to 0.261 with the increase of mass fraction from 0 to 80 wt%. It also indicates that the novel method can accurately and efficiently measure the elasticity of droplets.

A Robust MP3 Steganographic Method against Multiple Compressions Based on Modified Discrete Cosine Transform

MP3 appears in various social networking sites wildly, and it is very suitable to be applied for covert communication indeed. However, almost all social networking sites recompress the uploaded MP3 files, which leads to the ineffectiveness of the existing MP3 steganographic methods. In this paper, a robust MP3 steganographic algorithm is proposed with the ability of multiple compressions resistance. First, we discover a new embedding domain with strong robustness. The scalefactor bands of higher energy are applied as the embedding bands. The message bits are embedded by adjusting the position of the MDCT coefficients with the largest magnitude in the embedding bands. Besides, the embedding and extraction operations are realized in the process of MP3 decoding at the same time. Experimental results illustrate that our proposed method is of strong robustness against multiple MP3 compressions. The bit error rate is less than 1% at the MP3 bitrate of 320 kbps. It is worth mentioning that the proposed method is proved to be applicable to social networking sites, such as SoundCloud, for covert communication. Our method achieves a satisfactory level of embedding capacity, imperceptibility, and undetectability.

BEvote: Bitcoin-Enabled E-Voting Scheme with Anonymity and Robustness

When building the large-scale distributed decision control system based on mobile terminal devices (MTDs), electronic voting (E-voting) is a necessary technique to settle the dispute among parties. Due to the inherent insecurity of Internet, it is difficult for E-voting to attain complete fairness and robustness. In this study, we argue that Bitcoin blockchain offers better options for a more practical E-voting. We first present a coin mixing-based E-voting system model, which can cut off the relationship between the voter’s real identity and its Bitcoin address to achieve strong anonymity. Moreover, we devise a secret sharing-based E-voting protocol, which can prevent voting number from being leaked ahead and further realize strong robustness. We establish the probable security theory to prove its security. In addition, we use the experimental evaluation to demonstrate its efficiency.

A Novel Method for Extracting Time Series Information of Deformation Area of a Single Landslide Based on Improved U-Net Neural Network

This paper proposed an improved U-Net fully convolutional neural network to automatically extract a single landslide deformation information under time series based on the physical model experiments. This method extracts time series information for three different landslide deformation ranges. Compared to U-Net and mainstream superpixel method, evaluation indicators of DSC, VOE and RVD verify the high recognition accuracy and strong robustness of our method.

Intelligent user identity authentication in vehicle security system based on wireless signals

Intelligent identity authentication in vehicle security systems, as a vital component in anti-theft system and safety driving assist system, has received wide attention. Current vehicle security systems, however, focus the car security on the car keys security, ignore the owner of car keys. Anyone who owns the car keys can operate the car. This paper introduces an intelligent identity authentication method for vehicle security system based on wireless signals. Unlike past work, our approach combines car security with car owners and car keys. The intuition underlying our design is that when a user walks towards the car, the user’s gait information can be leveraged to identify the user. We capture the user’s gait information using wireless devices which can be deployed in the car, and then extract features from the received wireless signals using convolution kernel and apply artificial neural network to identify the user. We built a prototype and experimental results show that our approach can achieve high accuracy and strong robustness.

Dual-mode of topological rainbow in gradual photonic heterostructures

In this letter, a method to realize the topological rainbow trapping is presented, which is composed of gradual ordinary-topological-ordinary heterostructures based on two-dimensional photonic crystals with C-4 symmetry. In the proposed sandwiched structure, the two coupled topological edge states with different frequencies are separated and trapped in different positions, due to group velocity of near to zero. We have achieved the dual-mode of topological rainbow in one structure, which broadens the bandwidth. Besides, the dual-mode of topological rainbow under one mode excitation is also realized by using a simple bend design. The immunity to defects is also investigated and it is found our slowing light system has strong robustness. Finite Element Method simulation results verify our idea, and our work opens up a new way for frequency routing and broadband operation of topological photonic states.

Improved visual inertial odometry based on deep learning

The traditional visual inertial odometry according to the manually designed rules extracts key points. However, the manually designed extraction rules are easy to be affected and have poor robustness in the scene of illumination and perspective change, resulting in the decline of positioning accuracy. Deep learning methods show strong robustness in key point extraction. In order to improve the positioning accuracy of visual inertial odometer in the scene of illumination and perspective change, deep learning is introduced into the visual inertial odometer system for key point detection. The encoder part of MagicPoint network is improved by depthwise separable convolution, and then the network is trained by self-supervised method; A visual inertial odometer system based on deep learning is compose by using the trained network to replace the traditional key points detection algorithm on the basis of VINS. The key point detection network is tested on HPatches dataset, and the odometer positioning effect is evaluated on EUROC dataset. The results show that the improved visual inertial odometer based on deep learning can reduce the positioning error by more than 5% without affecting the real-time performance.

An Image Encryption Algorithm Based on the Improved Sine-Tent Map

This paper makes use of the characteristics of initial sensitivity and randomness of the chaotic map to design an image encryption algorithm based on the sine map and the tent map. The sine map is used to improve the tent map; then, the improved sine-tent map is proposed. The traditional tent map proposed in this paper has an expanded control parameter range and better chaos. In this algorithm, bit rearrangement is adopted to further improve the improved sine-tent map, which can reconstruct the output value and expand the chaotic characteristics of the map. In this algorithm, the image parameters are connected with the algorithm to generate the key. In the encryption step, a method of replacing the most significant bit and scrambling-diffusion algorithm is designed to encrypt the plaintext image. Finally, the algorithm is simulated with the experiment and evaluated with analysis; then, the experimental results are given. The evaluation results show that the ciphertext of the algorithm has high randomness, strong robustness, and better resistance to differential attacks after comparison. The correlation of the ciphertext image pixels is very low, and the algorithm is highly secure as a conclusion.