To solve the problem that the influencing factors of crowdsourced testing are complex and diverse, and the test quality is difficult to assess, a method for analyzing the quality influencing factors based on Spearman correlation coefficient was proposed. Firstly, the potential quality influencing factors were obtained through the analysis of test platforms, tasks, and testers. Secondly, Spearman correlation coefficient was used to calculate the correlation degrees between potential factors and test quality and to screen out key factors. Finally, the multiple stepwise regression was used to establish a linear evaluation relationship between key factors and test quality. The experimental results show that compared with the traditional expert artificial evaluation method, the proposed method can maintain smaller fluctuations in evaluation error when facing a large number of test tasks. Therefore, the method can accurately screen out the key influencing factors of mobile application crowdsourced test quality.

The quality of the molten steel is usually judged by the hit rate of the endpoint. However, there are many influencing factors in the steelmaking process, and it is difficult to accurately predict the endpoint temperature and carbon content. In view of this, a data-driven Multi-Task Learning (MTL) steelmaking endpoint prediction method was proposed. Firstly, the input and output factors of steelmaking process were analyzed and extracted, and a number of sub-learning tasks were selected to combine the two-stage blowing characteristics of steelmaking. Secondly, according to the relativity between the sub-tasks and the endpoint parameters, the appropriate subtasks were selected to improve the accuracy of the endpoint prediction, and the multi-task learning model was constructed, and the model output was optimized twice. Finally, the process parameters of the multitask learning model were obtained by model training of the processed production data through the proximal gradient algorithm. In the case of a steel plant, compared with neural network, the prediction accuracy of the method proposed increased 10% when endpoint temperature error was less than 12℃ and carbon content error was less than 0.01%. The prediction accuracy increased by 11% and 7% respectively with the error range less than 6℃ and 0.005%. The experimental results show that multi-task learning can improve the accuracy of endpoint prediction in practice.

In view of poor denoising performance and unideal speed of the current median filter, a fast and Adaptive Four-dot Midpoint Filter (AFMF) was proposed. Firstly, noise pixels and non-noise pixels of an image were identified using a simple extreme method to reduce the computational complexity. Then, the traditional full-point window was discarded, instead of median filtering, but on the basis of switch filtering and clipping filtering, a new nonlinear filtering method named midpoint filtering was adopted to simplify the algorithm flow, improve the calculation efficiency, improve the denoising effect. Finally, starting from a 3×3 window from inside to outside, the window was gradually enlarged to form adaptive filtering, until all the noise pixels were processed, the setting of window size parameters was avoided. The experimental results show that compared with AMF, SAMF, MDBUTMF and DBCWMF, AFMF not only has better denoising performance but also faster operation speed (about 0.18 s), but also does not need to set parameters, which is easy to operate and has strong practicability.

In order to solve the problems of the Social Learning Particle Swarm Optimization (SLPSO) algorithm, such as slow convergence speed and low search efficiency, a Cross opposition learning and Particle-based social learning Particle Swarm Optimization (CPPSO) algorithm was proposed. Firstly, a cross opposition learning mechanism was formulated based on combining general opposition learning, random opposition learning and vertical random cross on the optimal solution. Secondly, the cross opposition learning was adopted for the optimal particle to improve the population diversity, exploration ability and avoid the disadvantage of SLPSO's slow convergence and low search efficiency. Finally, a novel social learning mechanism was adopted for the non-optimal particles in the particle swarm, and the new social learning method used particle-based approach, instead of the dimension-based one of SLPSO, not only improved the exploration capacity, but also improved exploitation and the optimization efficiency. The simulation results on a set of benchmark functions with different dimensions show that the optimization performance, search efficiency and generalizability of the CPPSO algorithm are much better than those of the SLPSO and the advanced PSO algorithms such as Crisscross Search PSO (CSPSO), Self-Regulating PSO (SRPSO), Heterogeneous Comprehensive Learning PSO (HCLPSO) and Reverse learning and Local learning PSO (RLPSO).

Aiming at the deficiencies of the current filters in removing salt-and-pepper noise from images, such as low denoising performance and slow running speed, an image denosing method based on Iterative Adaptive Weighted-mean Filter (IAWF) was proposed. Firstly, a new method was used to construct the neighborhood weight by using the similarity between the neighborhood pixels and the processed point. Then a new weighted-mean filter algorithm was formed by combing the neighborhood weight with switching trimmed mean filter, making full use of the correlation of the image pixels and the advantages of switching trimmed filter, effectively improving the denoising effect. At the same time, the window size of the filter was automatically adjusted to protect the details as much as possible. Finally, the iterative filter was applied to continue until the noisy points were processed completely in order to process automatically and reduce manual intervention. The simulation results show that compared with several state-of-the-art denoising algorithms, the proposed algorithm is better in Peak Signal-to-Noise Ratio (PSNR), collateral distortion and subjective denoising effect under various noise densities, with higher denoising speed, more suitable for practical applications.

In adaptive beamforming, the presence of the desired signal component in the training data, small sample size, and imprecise knowledge of the desired signal steering vector are the main causes of performance degradation. In order to solve this problem, this paper proposed a robust adaptive beamforming algorithm which performed interference-plus-noise covariance matrix reconstruction and desired signal steering vector estimation. In this algorithm, first the interference-plus-noise covariance matrix was reconstructed using Multiple Signal Classification (MUSIC) spatial spectrum in the signal-free angle section, then the constraint that prevented the convergence of the estimate of the desired signal steering vector to any of the interference steering vectors or their linear combination was derived, next this constraint was used together with the maximization of the array output power to formulate an optimization problem of estimating the desired signal steering vector, and convex optimization software was used to yield the desired signal steering vector. In the paper, the computational complexity of the proposed method was discussed and its effectiveness and superiority were validated by simulations. The simulation results demonstrate that the Signal to Interference plus Noise Ratio (SINR) of proposed adaptive beamformer is almost always close to optimal in a very large range of Signal-to-Noise Ratio (SNR) in the scenarios of random signal and interference look direction mismatch and incoherent local scattering, which is more robust than the existing beamformers.

The existing watermarking algorithm based on image moments has the disadvantages of small capacity, large complexity and its robustness should be improved in further study. A new zero-watermarking against geometric attacks was proposed. Using the image normalization and the features of Tchebichef moments, the rotation normalized Tchebichef moments of original image was calculated in the unit circle, and the upper-left corner of Tchebichef moments was scanned into numerical matrix. Afterwards, binary secret key was generated according to numerical matrix and watermark image, and saved to zero-watermarking information database. In detection, the same process was executed to draw out numerical matrix from the unauthenticated image, and watermark image was extracted by using the secret key and numerical matrix. The experimental results show that this algorithm is robust against rotation attacks of random angles, scaling attacks and common signal processing operations, even combined attacks.