Random Target Localization for an Upper Limb Prosthesis

To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5-DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

Wisdom of crowds benefits perceptual decision making across difficulty levels

Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent group decisions. Our study consisted of a random target detection task in natural scenes, where human subjects (18 subjects, 7 female) detected the presence or absence of a random target as indicated by the cue word displayed prior to stimulus display. Concurrently the neural activities (EEG signals) were recorded. A separate behavioural experiment was performed by different subjects (20 subjects, 11 female) on the same set of images to categorize the tasks according to their difficulty levels. We demonstrate that the weighted average of individual decision confidence/neural decision variables produces significantly better performance than the frequently used majority pooling algorithm. Further, the classification error rates from individual judgement were found to increase with increasing task difficulty. This error could be significantly reduced upon combining the individual decisions using group aggregation rules. Using statistical tests, we show that combining all available participants is unnecessary to achieve minimum classification error rate. We also try to explore if group aggregation benefits depend on the correlation between the individual judgements of the group and our results seem to suggest that reduced inter-subject correlation can improve collective decision making for a fixed difficulty level.

Adaptive filter based on Monte Carlo method to improve the target tracking in radar systems

The state estimation and tracking of random target is an attractive research problem in radar system. The information received in the radar receiver was reflected by the target, that it is received with many white and Gaussian noise due to the characteristics of the transmission channel and the radar environment. After detection and location scenarios, the radar system must track the target in real time. We aim to improve the state estimation process for too random target at the given instant in order to converge to the true target state and smooth their true path for a long time, it simplifies the process of real-time tracking. In this framework, we propose a new approach based on the numerical methods presented by MONTE CARLO (MC) counterpart the method conventionally used named Extended KALMAN Filter (EKF), we showed that the first are more successful. Keywords: Radar, Monte Carlo, Extended KALMAN Filter, Tracking, PF, Random target.

Study on the Optimal Distribution Strategy of Multi-Firepower Units Based on Markov Model

Under the circumstances of informationized war, how to distribute firepower units in the most economic manner to maximize the performance of combat system as a whole has become the hot topic with which the combat agent is concerned. The conventional static method based on linear programming intends to distribute firepower units to the enemy targets under certain restrictions as possible as it can , when the number of enemy targets is more than that of the firepower units, there will be multiple firepower units acting to the same enemy target, the effectiveness of combat will be greatly reduced oppositely while the invasion intensity of enemy targets is becoming larger and stronger suddenly. The threat intensity of coming enemy target is included into performance indices for distribution by Markov random target dynamic distribution strategy. based on the original static distribution strategy proposed by this paper, and the weapons will be redistributed to other targets when one weapon finishes its shooting with no more targets distributed, and the less efficient weapons will be replaced with those more efficient ones. Thus, the system can still redistribute firepower units to enemy targets that haven’t been destroyed according to the current status of weapon system's firepower units, even though no new targets arrive or no new instructions of dynamic firepower targets’ distribution decisions are given. All these can help minimize the whole threatening intensity of enemy group, in order to realize the maximum of weapon system’s average economic benefit of combat in the long term.