Rongxin Jiang

Atomic dynamic functional interaction patterns for characterization of ADHD

Modeling abnormal temporal dynamics of functional interactions in psychiatric disorders has been of great interest in the neuroimaging field, and thus a variety of methods have been proposed so far. However, the temporal dynamics and disease‐related abnormalities of functional interactions within specific data‐driven discovered subnetworks have been rarely explored yet. In this work, we propose a novel computational framework composed of an effective Bayesian connectivity change point model for modeling functional brain interactions and their dynamics simultaneously and an effective variant of nonnegative matrix factorization for assessing the functional interaction abnormalities within subnetworks. This framework has been applied on the resting state fmagnetic resonance imaging (fMRI) datasets of 23 children with attention‐deficit/hyperactivity disorder (ADHD) and 45 normal control (NC) children, and has revealed two atomic functional interaction patterns (AFIPs) discovered for ADHD and another two AFIPs derived for NC. Together, these four AFIPs could be grouped into two pairs, one common pair representing the common AFIPs in ADHD and NC, and the other abnormal pair representing the abnormal AFIPs in ADHD. Interestingly, by comparing the abnormal AFIP pair, two data‐driven abnormal functional subnetworks are derived. Strikingly, by evaluating the approximation based on the four AFIPs, all of the ADHD children were successfully differentiated from NCs without any false positive. Hum Brain Mapp 35:5262–5278, 2014.

A Robot Collision Avoidance Scheme Based on the Moving Obstacle Motion Prediction

This paper proposes an integrated scheme for the robot to avoid collision based on obstaclepsilas motion prediction. The robot obtains the target bearing by using object detection technique and fuses the bearing information with the external sensors data to locate the current position of the moving target. The motion state of the moving obstacle is described using the constant velocity (CV) model, the constant accelerate (CA) model and the current statistical (CS) model. Then, a Kalman-based interacting multiple model (IMM) filter is adopted to estimate the obstacle motion trend. This scheme mainly focuses on the obstacle detection and the motion prediction. Based on the motion prediction of the obstacle, an abbreviated strategy is proposed for collision avoidance. To validate the proposed scheme, we have implemented an obstacle avoidance experiment with a Pioneer 3-AT robot and a moving obstacle. The results verify that the proposed scheme is valid and viable.

Sequential Asynchronous Filters for Target Tracking in Wireless Sensor Networks

Asynchronous data fusion is inevitable for target tracking in asynchronous wireless sensor networks, where multiple sensors are required to locate a target collaboratively. The predicted estimates of the follow-up states based on the to-be-estimated state are first introduced to overcome the drawback that asynchronous measurements cannot be fused directly. Then, the sequential asynchronous Bayesian state estimation is deduced based on the predicted estimates. The proposed estimation process is comprised of two steps: 1) the prediction step and 2) the update step. Finally, sequential asynchronous filters based Kalman filter and particle filter are proposed. Simulations demonstrate that the proposed algorithms perform not only better than the benchmark algorithms with asynchronous measurements, but also better than the benchmark algorithm with synchronous measurements.

Rate-distortion based reference viewpoints selection for multi-view video plus depth coding

For multi-view video plus depth (MVD) coding in 3D video (3DV) applications, texture videos and depth maps of a few reference viewpoints should be compressed and transmitted. The other intermediate virtual viewpoints are synthesized by depth-image-based rendering algorithm. The videos displayed for terminal users include the decoded reference viewpoints and the synthesized virtual viewpoints. Under a given bit rate constraint, how to choose the optimal reference viewpoints setting to obtain the best video quality of all display viewpoints is an important research issue. This paper proposes a rate distortion model based algorithm to obtain the optimal reference viewpoints selection. First, the impacts of the reference viewpoints setting on the entire quality of all display viewpoints is investigated, and then a quadratic distortion model for all display viewpoints is derived. Based on this distortion model, the optimal viewpoints selection problem is solved by computing a few model parameters. Experimental results demonstrate that the proposed distortion model estimates the actual distortion of all display viewpoints with high accuracy, and the proposed reference viewpoints selection method achieves almost the same rate distortion performance as compared to the full search method with a very low computational cost.

A Real-Time 3-D Underwater Acoustical Imaging System

Real-time 3-D acoustical imaging technique is a key advance to broaden the scope and enhance the feasibility of underwater missions. In this paper, a real-time 3-D underwater acoustical imaging system to handle the actual tasks under a narrowband excitation is presented. The system consists of three parts: a transmitter, a receiving hydrophone array, and a signal processor. In this system, a distributed and parallel subarray (DPS) beamforming algorithm is proposed to process the acquired signals from a scene placed in the far field. The DPS beamforming algorithm is an approximate method for the sonar signal processing with an advantageous computational efficiency. The direct method (DM) and fast Fourier transform (FFT) beamforming are compared with DPS beamforming for the memory and computational requirements. Based on this algorithm, a prototype was developed, which has been extensively employed in the lake and sea trials. The trials demonstrate that the system can achieve the 3-D imaging of the scene and meet the real-time requirement of the underwater operations.

Modeling brain functional dynamics via hidden Markov models

Functional connectivities constructed via resting state fMRI (R-fMRI) data have been widely used to study the brains functional activities and to characterize the brains states. However, the temporal dynamic transition patterns of the brains functional states have been rarely investigated before. In this paper, we present a novel algorithmic framework to cluster and label the brains functional states, and learn their hidden Markov models (HMMs). Here, the brains functional state is compactly represented by a large-scale functional connectivity matrix, called functional connectome state (FCS), and the temporal FCS sequences are derived via an overlapping sliding time window approach. The best-matched HMM learned for ADHD patients revealed a meaningful phenomenon of psychiatric conditions, that is, the tendency to enter into, and inability to disengage from, a negative mood state. Experimental results demonstrated 87% of ADHD patients and 89% of normal controls are successfully classified via multiple HMMs by using majority voting.

Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment

In recent years, functional connectomics signatures have been shown to be a very valuable tool in characterizing and differentiating brain disorders from normal controls. However, if the functional connectivity alterations in a brain disease are localized within sub-networks of a connectome, then accurate identification of such disease-specific sub-networks is critical and this capability entails both fine-granularity definition of connectome nodes and effective clustering of connectome nodes into disease-specific and non-disease-specific sub-networks. In this work, we adopted the recently developed DICCCOL (dense individualized and common connectivity-based cortical landmarks) system as a fine-granularity high-resolution connectome construction method to deal with the first issue, and employed an effective variant of non-negative matrix factorization (NMF) method to pinpoint disease-specific sub-networks, which we called atomic connectomics signatures in this work. We have implemented and applied this novel framework to two mild cognitive impairment (MCI) datasets from two different research centers, and our experimental results demonstrated that the derived atomic connectomics signatures can effectively characterize and differentiate MCI patients from their normal controls. In general, our work contributed a novel computational framework for deriving descriptive and distinctive atomic connectomics signatures in brain disorders.

A Low-Complexity Real-Time 3-D Sonar Imaging System With a Cross Array

The development of real-time 3-D underwater imaging is restricted by the huge hardware cost and the computational burden associated with a large number of transducers. In this paper, a low-complexity real-time 3-D sonar imaging system with a cross array is proposed. The low complexity, in both hardware cost and computational load, is achieved by an innovative method of signal processing in the far field and the advantages of the cross array in minimal transducer numbers. The method consists of two parts: a multifrequency (MF) algorithm for the transmitting process and a parallel subarray (PS) algorithm for the receiving beamforming. The MF algorithm solves the real-time problem of the cross array by reducing the scanning time that is proportional to the number of transmissions. The PS algorithm improves the computational efficiency using a two-stage parallel and pipeline framework. The PS algorithm is an approximate method, and its precision is analyzed in this paper. The computational efficiency of the proposed method is compared with the direct method (DM) beamforming in both cross and planar arrays. A prototype, based on the innovative method, was designed and tested in pool and lake trials. The results demonstrate that the low-complexity system can satisfy the real-time requirement of 3-D underwater imaging applications with an acceptable imaging quality.

Probabilistic 3D ICP algorithm based on ORB feature

Aligning the 3D point clouds is considered as a crucial step to build consistent maps from unknown environment in SLAM problem of mobile robotics. However, ICP algorithms for aligning the point clouds usually ignore the valuable visual information contained in the point clouds and only model surface structure from the “model” scan. This paper presents a new ICP algorithm incorporating the visual ORB feature. This approach is based on a probabilistic ICP algorithm that takes into account both scans for RGB images along with the depth information from Kinect sensor. Experiments are carried out on real world scenes and results show that the new approach improves the accuracy and saves time of the registration.

Adaptive square-root transformed unscented FastSLAM with KLD-resampling

ABSTRACT The FastSLAM relies on particles sampled from the proposal distribution of underlying Rao–Blackwellized particle filter, and its performance is significantly influenced by the quality and quantity of the particles. In this paper, a new improved FastSLAM is proposed based on transformed unscented Kalman filter (TUKF) and Kullback–Leibler distance (KLD) resampling method. In the proposed algorithm, a square-root extension of TUKF is used to calculate the proposal distribution and to generate credible particles. In addition, during the resampling process, the minimum required number of particles is determined adaptively by bounding the KLD error between the sample-based approximation and true posterior distribution of the robot state. Both numerical simulations and real-world dataset experiments are used to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm achieves higher estimation accuracy and computational efficiency than conventional approaches.