Jianfeng Zhou

Yunqi Sun, Jianfeng Zhou

The sampling effect of the imaging acquisition device is long considered to be a modulation process of the input signal, introducing additional error into the signal acquisition process. This paper proposes a correction algorithm for the modulation process that solves the sampling effect with high accuracy. We examine the algorithm with perfect continuous Gaussian images and selected digitized images, which indicate an accuracy increase of 106 for Gaussian images, 102 at 15 times of Shannon interpolation for digitized images, and 105 at 101 times of Shannon interpolation for digitized images. The accuracy limit of the Gaussian image comes from the truncation error, while the accuracy limit of the digitized images comes from their finite resolution, which can be improved by increasing the time of Shannon interpolation.

Nan Xu, Zepeng Tang, Hassan Askari et al.

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the slip ratio reference value can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, machine learning (ML) algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which create new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation.

Jianfeng Zhou, Tao Jiang, Lin Li et al.

Under noisy environments, to achieve the robust performance of speaker recognition is still a challenging task. Motivated by the promising performance of multi-task training in a variety of image processing tasks, we explore the potential of multi-task adversarial training for learning a noise-robust speaker embedding. In this paper we present a novel framework which consists of three components: an encoder that extracts noise-robust speaker embedding; a classifier that classifies the speakers; a discriminator that discriminates the noise type of the speaker embedding. Besides, we propose a training strategy using the training accuracy as an indicator to stabilize the multi-class adversarial optimization process. We conduct our experiments on the English and Mandarin corpus and the experimental results demonstrate that our proposed multi-task adversarial training method could greatly outperform the other methods without adversarial training in noisy environments. Furthermore, experiments indicate that our method is also able to improve the speaker verification performance the clean condition.

Yaodong He, Jianfeng Zhou, Shiu Yin Yuen

Photomosaic images are a type of images consisting of various tiny images. A complete form can be seen clearly by viewing it from a long distance. Small tiny images which replace blocks of the original image can be seen clearly by viewing it from a short distance. In the past, many algorithms have been proposed trying to automatically compose photomosaic images. Most of these algorithms are designed with greedy algorithms to match the blocks with the tiny images. To obtain a better visual sense and satisfy some commercial requirements, a constraint that a tiny image should not be repeatedly used many times is usually added. With the constraint, the photomosaic problem becomes a combinatorial optimization problem. Evolutionary algorithms imitating the process of natural selection are popular and powerful in combinatorial optimization problems. However, little work has been done on applying evolutionary algorithms to photomosaic problem. In this paper, we present an algorithm called clustering based evolutionary programming to deal with the problem. We give prior knowledge to the optimization algorithm which makes the optimization process converges faster. In our experiment, the proposed algorithm is compared with the state of the art algorithms and software. The results indicate that our algorithm performs the best.