Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS

There are increasing real-time live applications in virtual reality, where it plays an important role in capturing and retargetting 3D human pose. But it is still challenging to estimate accurate 3D pose from consumer imaging devices such as depth camera. This paper presents a novel cascaded 3D full-body pose regression method to estimate accurate pose from a single depth image at 100 fps. The key idea is to train cascaded regressors based on Gradient Boosting algorithm from pre-recorded human motion capture database. By incorporating hierarchical kinematics model of human pose into the learning procedure, we can directly estimate accurate 3D joint angles instead of joint positions. The biggest advantage of this model is that the bone length can be preserved during the whole 3D pose estimation procedure, which leads to more effective features and higher pose estimation accuracy. Our method can be used as an initialization procedure when combining with tracking methods. We demonstrate the power of our method on a wide range of synthesized human motion data from CMU mocap database, Human3.6M dataset and real human movements data captured in real time. In our comparison against previous 3D pose estimation methods and commercial system such as Kinect 2017, we achieve the state-of-the-art accuracy

A probabilistic data association based MIMO detector using joint detection of consecutive symbol vectors

A new probabilistic data association (PDA) approach is proposed for symbol detection in spatial multiplexing multiple-input multiple-output (MIMO) systems. By designing a joint detection (JD) structure for consecutive symbol vectors in the same transmit burst, more a priori information is exploited when updating the estimated posterior marginal probabilities for each symbol per iteration. Therefore the proposed PDA detector (denoted as PDA-JD detector) outperforms the conventional PDA detectors in the context of correlated input bit streams. Moreover, the conventional PDA detectors are shown to be a special case of the PDA-JD detector. Simulations and analyses are given to demonstrate the effectiveness of the new method

Simulated annealing based multiuser detection for synchronous SDMA system

In this treatise, a novel Simulated Annealing (SA) based Multi-User Detection (MUD) is proposed in synchronous Space Division Multiple Access (SDMA) system. SA MUD modifies experiential Cooling Schedule (CS) of traditional SA algorithm according to its use in MUD. Moreover, in order to ensure sufficient diversity acquired in the whole Markov chain and to prevent from being trapped at local optima, Uniform Mutation (UM) based trial vector generation scheme is brought forward. In addition, the optimal solution recording scheme is also invoked in case of being lost during cooling process. Simulation results illustrate that in comparison with Genetic Algorithm (GA) MUD in the same simulation conditions, without turbo processing and soft-information, SA MUD proposed in this paper performs better, approaching the performance of Maximum Likelihood (ML) MUD and imposes lower complexity