Gait Recognition

Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced

Resonance-induced acceleration of the RBNE-BNE segregation inversion of granular mixtures

This paper presents the experiments and simulations on the resonance-induced acceleration of the reverse Brazil nut effect (RBNE)-Brazil nut effect (BNE) segregation inversion of binary mixtures in flat-bottom and circular-bottom containers. Both experimental and simulation results indicate that the starting location of the sinkage of heavier grains at the top layer is triggered with certain randomness in the flat-bottom container, whereas it first occurs at either of the lateral edges of the bottom in the circular-bottom container. The quantified segregation factors in simulations show that the transition from the RBNE segregation state to the BNE segregation state happens faster in the circular-bottom container than that in the flat-bottom container. The occurrence of standing-wave resonant spots of higher and lower granular temperature accelerates the RBNE-BNE segregation inversion. From the elastic collision model of single grain, the bottom with a larger angle leads to more energy transfer from the vertical direction to the horizontal direction. The theoretical predictions are confirmed by the simulations of a monodisperse granular bed. The flat-bottom container has a uniform distribution with a standing-wave period of granular temperature and packing density, whereas the circular-bottom container possesses a higher granular temperature in the horizontal direction and a lower packing density at the lateral edges of the circular bottom. Owing to the buoyancy effect, heavier grains easily sink first at the resonant spots with higher temperature.Comment: 12 pages, 10 figure

Imaging the Centromedian Thalamic Nucleus Using Quantitative Susceptibility Mapping.

The centromedian (CM) nucleus is an intralaminar thalamic nucleus that is considered as a potentially effective target of deep brain stimulation (DBS) and ablative surgeries for the treatment of multiple neurological and psychiatric disorders. However, the structure of CM is invisible on the standard T1- and T2-weighted (T1w and T2w) magnetic resonance images, which hamper it as a direct DBS target for clinical applications. The purpose of the current study is to demonstrate the use of quantitative susceptibility mapping (QSM) technique to image the CM within the thalamic region. Twelve patients with Parkinson's disease, dystonia, or schizophrenia were included in this study. A 3D multi-echo gradient recalled echo (GRE) sequence was acquired together with T1w and T2w images on a 3-T MR scanner. The QSM image was reconstructed from the GRE phase data. Direct visual inspection of the CM was made on T1w, T2w, and QSM images. Furthermore, the contrast-to-noise ratios (CNRs) of the CM to the adjacent posterior part of thalamus on T1w, T2w, and QSM images were compared using the one-way analysis of variance (ANOVA) test. QSM dramatically improved the visualization of the CM nucleus. Clear delineation of CM compared to the surroundings was observed on QSM but not on T1w and T2w images. Statistical analysis showed that the CNR on QSM was significantly higher than those on T1w and T2w images. Taken together, our results indicate that QSM is a promising technique for improving the visualization of CM as a direct targeting for DBS surgery

An Adaptive Self-Interference Cancelation/Utilization and ICA-Assisted Semi-Blind Full-Duplex Relay System for LLHR IoT

In this article, we propose a semi-blind full-duplex (FD) amplify-and-forward (AF) relay system with adaptive self-interference (SI) processing assisted by independent component analysis (ICA) for low-latency and high-reliability (LLHR) Internet of Things (IoT). The SI at FD relay is not necessarily canceled as much as possible like the conventional approaches, but is canceled or utilized based on a signal-to-residual-SI ratio (SRSIR) threshold at relay. According to the selected SI processing mode at relay, an ICA-based adaptive semi-blind scheme is proposed for signal separation and detection at destination. The proposed FD relay system not only features reduced signal processing cost of SI cancelation but also achieves a much higher degree of freedom in signal detection. The resulting bit error rate (BER) performance is robust against a wide range of SRSIR, much better than that of conventional FD systems, and close to the ideal case with perfect channel state information (CSI) and perfect SI cancelation. The proposed system also requires negligible spectral overhead as only a nonredundant precoding is needed for ambiguity elimination in ICA. In addition, the proposed system enables full resource utilization with consecutive data transmission at all time and same frequency, leading to much higher throughput and energy efficiency than the time-splitting and power-splitting-based self-energy recycling approaches that utilize only partial resources. Furthermore, an intensive analysis is provided, where the SRSIR thresholds for the adaptive SI processing mode selection and the BER expressions with ICA incurred ambiguities are derived