Path planning of mobile robot based on improved TD3 algorithm in dynamic environment.

This paper proposes an improved TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm to address the flaws of low success rate and slow training speed, when using the original TD3 algorithm in mobile robot path planning in dynamic environment. Firstly, prioritized experience replay and transfer learning are introduced to enhance the learning efficiency, where the probability of beneficial experiences being sampled in the experience pool is increased, and the pre-trained model is applied in an obstacle-free environment as the initial model for training in a dynamic environment. Secondly, dynamic delay update strategy is devised and OU noise is added to improve the success rate of path planning, where the probability of missing high-quality value estimate is reduced through changing the delay update interval dynamically, and the correlated exploration of the mobile robot inertial navigation system in the dynamic environment is temporally improved.

Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion.

Convergence speed and steady-state source separation performance are crucial for enable engineering applications of blind source separation methods. The modification of the loss function of the blind source separation algorithm and optimization of the algorithm to improve its performance from the perspective of neural networks (NNs) is a novel concept. In this paper, a blind source separation method, combining the maximum likelihood estimation criterion and an NN with a bias term, is proposed.

Electro-magnetic analysis of high-frequency digital signal processors.

High-frequency digital signal processors are increasingly suffering from electro-magnetic interference, due to its ever-increasing integration level and operation speed. The accurate prediction of its electro-magnetic effects require less effort to be spared in the design procedures to obtain better electro-magnetic compatibility and to avoid later modifications that are lengthy and expensive. In this paper, the dipole method is implemented to predict the magnetic impacts of DSP6713 system in order to reduce its design costs.

Sequence-specific DNA detection by using biocatalyzed electrochemiluminescence and non-fouling surfaces.

We herein report a DNA sensor for the sequence-specific DNA detection by using glucose oxidase-based biocatalyzed electrochemiluminescence and non-fouling surfaces. In this design, a glucose oxidase-labeled sandwich-type DNA sensor was built on a non-fouling surfaces made of a mixed self-assembled monolayers (SAMs) incorporating thiolated oligonucleotides and oligo(ethylene glycol) (OEG) thiols (SH-DNA/OEG). The sequence-specific DNA sensing was accomplished by the electrochemiluminesce (ECL) signal of luminol with the in-situ generated H(2)O(2).

Sequence-specific detection of femtomolar DNA via a chronocoulometric DNA sensor (CDS): effects of nanoparticle-mediated amplification and nanoscale control of DNA assembly at electrodes.

We herein report a novel nanoparticle-based electrochemical DNA detection approach. This DNA sensor is based on a "sandwich" detection strategy, which involves capture probe DNA immobilized on gold electrodes and reporter probe DNA labeled with gold nanoparticles that flank the target DNA sequence. Electrochemical signals are generated by chronocoulometric interrogation of [Ru(NH(3))(6)](3+) that quantitatively binds to surface-confined capture probe DNA via electrostatic interactions.