Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

Intracoronary Sarcoplasmic Reticulum CalciumATPase Gene Therapy in Advanced Heart Failure Patients with reduced Ejection Fraction: A Prospective Cohort Study

OBJECTIVE: Heart failure is a progressive and debilitating disease. Intracoronary sarcoplasmic reticulum calciumATPase gene therapy may improve the function of cardiac muscle cells. This study aimed to test the hypothesis that intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy can improve outcomes and reduce the number of recurrent and terminal events in advanced heart failure patients with reduced ejection fraction. METHODS: A total of 768 heart failure patients with reduced ejection fraction and New York Heart Association classification II to IV were included in this prospective cohort study. Patients either underwent intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy (CA group, n=384) or received oral placebo (PA group; n=384). Data regarding recurrent and terminal event(s), treatment-emergent adverse effects, and outcome measures were collected and analyzed. RESULTS: After a follow-up period of 18 months, intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy reduced the number of hospital admissions (p=0.001), ambulatory treatments (p=0.0004), and deaths (p=0.024). Additionally, intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy improved the left ventricular ejection fraction (po0.0001) and Kansas City Cardiomyopathy Questionnaire score (po0.0001). The number of recurrent and terminal events/patients were higher in the PA group than in the CA group after the follow-up period of 18 months (p=0.015). The effect of the intracoronary sarcoplasmic reticulum calciumATPase gene therapy was independent of the confounding variables. No new arrhythmias were reported in the CA group. CONCLUSIONS: Intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy reduces the number of recurrent and terminal events and improves the clinical course of advanced heart failure patients with reduced ejection fraction

Current sustainability and electromigration of Pd, Sc and Y thin-films as potential interconnects

The progress on novel interconnects for carbon nanotube (CNT)-based electronic circuit is by far behind the remarkable development of CNT-field effect transistors. The Cu interconnect material used in current integrated circuits seems not applicable for the novel interconnects, as it requires electrochemical deposition followed by chemical-mechanical polishing. We report our experimental results on the failure current density, resistivity, electromigration effect and failure mechanism of patterned stripes of Pd, Sc and Y thin-films, regarding them as the potential novel interconnects. The Pd stripes have a failure current density of (8 similar to 10)x10(6) A/cm(2) (MA/cm(2)), and they are stable when the working current density is as much as 90% of the failure current density. However, they show a resistivity around 210 mu O.cm, which is 20 times of the bulk value and leaving room for improvement. Compared to Pd, the Sc stripes have a similar resistivity but smaller failure current density of 4 similar to 5 MA/cm(2). Y stripes seem not suitable for interconnects by showing even lower failure current density than that of Sc and evidence of oxidation. For comparison, Au stripes of the same dimensions show a failure current density of 30 MA/cm(2) and a resistivity around 4 mu O.cm, making them also a good material as novel interconnects.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000208414400008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Nanoscience & NanotechnologyMaterials Science, MultidisciplinaryPhysics, AppliedSCI(E)2ARTICLE3184-189

Green communication in energy renewable wireless mesh networks: routing, rate control, and power allocation

PublishedJournal Article© 2014 IEEE. The increasing demand for wireless services has led to a severe energy consumption problem with the rising of greenhouse gas emission. While the renewable energy can somehow alleviate this problem, the routing, flow rate, and power still have to be well investigated with the objective of minimizing energy consumption in multi-hop energy renewable wireless mesh networks (ER-WMNs). This paper formulates the problem of network-wide energy consumption minimization under the network throughput constraint as a mixed-integer nonlinear programming problem by jointly optimizing routing, rate control, and power allocation. Moreover, the min-max fairness model is applied to address the fairness issue because the uneven routing problem may incur the sharp reduction of network performance in multi-hop ER-WMNs. Due to the high computational complexity of the formulated mathematical programming problem, an energy-aware multi-path routing algorithm (EARA) is also proposed to deal with the joint control of routing, flow rate, and power allocation in practical multi-hop WMNs. To search the optimal routing, it applies a weighted Dijkstra's shortest path algorithm, where the weight is defined as a function of the power consumption and residual energy of a node. Extensive simulation results are presented to show the performance of the proposed schemes and the effects of energy replenishment rate and network throughput on the network lifetime

Weighted Multimodel Predictive Function Control for Automatic Train Operation System

Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm

The Development of a Portable Hard Disk Encryption/Decryption System with a MEMS Coded Lock

In this paper, a novel portable hard-disk encryption/decryption system with a MEMS coded lock is presented, which can authenticate the user and provide the key for the AES encryption/decryption module. The portable hard-disk encryption/decryption system is composed of the authentication module, the USB portable hard-disk interface card, the ATA protocol command decoder module, the data encryption/decryption module, the cipher key management module, the MEMS coded lock controlling circuit module, the MEMS coded lock and the hard disk. The ATA protocol circuit, the MEMS control circuit and AES encryption/decryption circuit are designed and realized by FPGA(Field Programmable Gate Array). The MEMS coded lock with two couplers and two groups of counter-meshing-gears (CMGs) are fabricated by a LIGA-like process and precision engineering method. The whole prototype was fabricated and tested. The test results show that the user's password could be correctly discriminated by the MEMS coded lock, and the AES encryption module could get the key from the MEMS coded lock. Moreover, the data in the hard-disk could be encrypted or decrypted, and the read-write speed of the dataflow could reach 17 MB/s in Ultra DMA mode