Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

Distributed adaptive direct position determination based on diffusion framework

The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.

Buck converter stability techniques for secondary filters and adaptive voltage positioning

To reduce output voltage noise and improve dynamic response performance,this study designed a buck converter on the basis of secondary filters and adaptive voltage positioning(AVP).A hybrid control method was proposed for the compensation of the secondary filter.The introduction of a high-frequency feedback path,in addition to the traditional feedback path,effectively improved the influence of the secondary filter on the loop stability and direct current regulation performance.A small-signal model of the buck converter based on the proposed control method was derived,and the stability and selection of control parameters were analyzed.AVP is realized using an easy-to-implement and low-cost control method that was proposed to improve dynamic response performance by changing the low-frequency gain of the control loop and load regulation of the output voltage.The experimental results of the buck converter showed that the proposed method effectively reduced the output voltage noise by 50%and improved the dynamic response capability to meet the target requirements of mainstream electronic systems.

Generalized multiple time windows model based parallel machine scheduling for TDRSS

The scheduling efficiency of the tracking and data relay satellite system（TDRSS）is strictly limited by the scheduling degrees of freedom（DoF）,including time DoF defined by jobs＇ flexible time windows and spatial DoF brought by multiple servable tracking and data relay satellites（TDRSs）.In this paper,ageneralized multiple time windows（GMTW）model is proposed to fully exploit the time and spatial DoF.Then,the improvements of service capability and job-completion probability based on the GMTW are theoretically proved.Further,an asymmetric path-relinking（APR）based heuristic job scheduling framework is presented to maximize the usage of DoF provided by the GMTW.Simulation results show that by using our proposal 11%improvement of average jobcompletion probability can be obtained.Meanwhile,the computing time of the time-to-target can be shorten to 1/9 of the GRASP.

Determining the imaging plane of a retinal capillary layer in adaptive optical imaging

Even in the early stage,endocrine metabolism disease may lead to micro aneurysms in retinal capillaries whose diameters are less than 10 μm.However,the fundus cameras used in clinic diagnosis can only obtain images of vessels larger than 20 μm in diameter.The human retina is a thin and multiple layer tissue,and the layer of capillaries less than10 μm in diameter only exists in the inner nuclear layer.The layer thickness of capillaries less than 10 μm in diameter is about 40 μm and the distance range to rod＆cone cell surface is tens of micrometers,which varies from person to person.Therefore,determining reasonable capillary layer（CL） position in different human eyes is very difficult.In this paper,we propose a method to determine the position of retinal CL based on the rod＆cone cell layer.The public positions of CL are recognized with 15 subjects from 40 to 59 years old,and the imaging planes of CL are calculated by the effective focal length of the human eye.High resolution retinal capillary imaging results obtained from 17 subjects with a liquid crystal adaptive optics system（LCAOS） validate our method.All of the subjects＇ CLs have public positions from 127 μm to 147 μm from the rod＆cone cell layer,which is influenced by the depth of focus.

Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small masses.We propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once(RCM-YOLO),which improves the identifiability of small masses by increasing the resolution of feature maps,adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters,and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature locations.In the training process,we propose an adaptive positive sample selection algorithm to automatically select positive samples,which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the model.To verify the performance of our model,we used public datasets to carry out the experiments.The results showed that the mean Average Precision(mAP)of RCM-YOLO reached 90.34%,compared with YOLOv5,the missed detection rate for small masses of RCM-YOLO was reduced to 11%,and the single detection time was reduced to 28 ms.The detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between features.Our method can help doctors in batch screening of breast images,and significantly promote the detection rate of small masses and reduce misdiagnosis.

Vision-based adaptive control of a 3-RRR parallel positioning system

The macro positioning stage with high-precision and rapid positioning ability plays a crucial role in the macro-micro combination positioning system. In this paper, we develop a practical method for the control of a 3-RRR planar positioning system using online vision measurement as feedback. In this method, a monocular vision system is established to accomplish highprecision online pose measurement for the 3-RRR manipulator. Additionally, a robust and operable adaptive control algorithm,which incorporates a fuzzy controller and a PI controller, is employed to achieve precise and rapid positioning of the 3-RRR positioning system. A series of experiments are conducted to verify the positioning performances of the proposed method, and a conventional PI control algorithm is utilized for comparison. The experimental results indicate that using the proposed control approach, the parallel positioning system obtains high precision and shows higher efficiency and robustness, especially for the time-varying positioning system.