Infrared small target energy distribution model for subpixel motion.

Dim small target detection is an important application of infrared (IR) searching and tracking systems. IR small target detection methods based on target energy information can accomplish a high detection rate and low false alarm rate. However, the general inaccuracy of target energy distribution models makes most detection algorithms ineffective. In this paper, considering the effect of a target's subpixel motion on energy distribution, a new, to the best of our knowledge, IR small target energy distribution (NSTED) model for subpixel motion is proposed. The NSTED model can well describe energy distribution variation of a subpixel moving target. The simulation results demonstrate that NSTED has a better output signal-to-noise ratio (SNR) than state-of-the-art Gaussian and hyperbolic secant models. Compared with the two models, NSTED improves the output SNR by at least 17.9% and 20.7%, respectively.

Goal-directed autonomous navigation of mobile robot based on the principle of neuromodulation.

Autonomous navigation in dynamic environment is aprerequisite of the mobile robot to perform tasks, and numerous approaches have been presented, including the supervised learning. Using supervised learning in robot navigation might meet problems, such as inconsistent and noisy data, and high error in training data. Inspired by the advantages of the reinforcement learning, such as no need for desired outputs, many researchers have applied reinforcement learning to robot navigation. This paper presents anovel method to address the robot navigation in different settings, through integrating supervised learning and analogical reinforcement learning into amotivated developmental network. We focus on the effect of the new learning rate on the robot navigation behavior. Experimentally, we show that the effect of internal neurons on the learning rate allows the agent to approach the target and avoid the obstacle as compounding effects of sequential states in static, dynamic, and complex environments. Further, we compare the performance between the emergent developmental network system and asymbolic system, as well as other four reinforcement learning algorithms. These experiments indicate that the reinforcement learning is beneficial for developing desirable behaviors in this set of robot navigation- staying statistically close to its target and away from obstacle.

Mexican Hat Wavelet Kernel ELM for Multiclass Classification.

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers.