Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018

Wireless Powered Cooperative Relaying using NOMA with Imperfect CSI

The impact of imperfect channel state (CSI) information in an energy harvesting (EH) cooperative non-orthogonal multiple access (NOMA) network, consisting of a source, two users, and an EH relay is investigated in this paper. The relay is not equipped with a fixed power source and acts as a wireless powered node to help signal transmission to the users. Closed-form expressions for the outage probability of both users are derived under imperfect CSI for two different power allocation strategies namely fixed and dynamic power allocation. Monte Carlo simulations are used to numerically evaluate the effect of imperfect CSI. These results confirm the theoretical outage analysis and show that NOMA can outperform orthogonal multiple access even with imperfect CSI.Comment: 6 pages, 6 figures, accepted in IEEE GLOBECOM 2018 NOMA Worksho

Enabling non-linear energy harvesting in power domain based multiple access in relaying networks: Outage and ergodic capacity performance analysis

The Power Domain-based Multiple Access (PDMA) scheme is considered as one kind of Non-Orthogonal Multiple Access (NOMA) in green communications and can support energy-limited devices by employing wireless power transfer. Such a technique is known as a lifetime-expanding solution for operations in future access policy, especially in the deployment of power-constrained relays for a three-node dual-hop system. In particular, PDMA and energy harvesting are considered as two communication concepts, which are jointly investigated in this paper. However, the dual-hop relaying network system is a popular model assuming an ideal linear energy harvesting circuit, as in recent works, while the practical system situation motivates us to concentrate on another protocol, namely non-linear energy harvesting. As important results, a closed-form formula of outage probability and ergodic capacity is studied under a practical non-linear energy harvesting model. To explore the optimal system performance in terms of outage probability and ergodic capacity, several main parameters including the energy harvesting coefficients, position allocation of each node, power allocation factors, and transmit signal-to-noise ratio (SNR) are jointly considered. To provide insights into the performance, the approximate expressions for the ergodic capacity are given. By matching analytical and Monte Carlo simulations, the correctness of this framework can be examined. With the observation of the simulation results, the figures also show that the performance of energy harvesting-aware PDMA systems under the proposed model can satisfy the requirements in real PDMA applications.Web of Science87art. no. 81

Investigation on energy harvesting enabled device-to-device networks in presence of co-channel interference

Energy harvesting from ambient radio-frequency (RF) sources has been a novel approach for extending the lifetime of wireless networks. In this paper, a cooperative device-to-device (D2D) system with the aid of energy-constrained relay is considered. The relays are assumed to be able to harvest energy from information signal and co-channel interference (CCI) signals broadcasted by nearby traditional cellular users and forward the source’s signal to its desired destination (D2D user) utilizing amplify-andforward (AF) relaying protocol. Time switching protocol (TSR) and power splitting protocol (PSR) are proposed to assist energy harvesting and information processing at the relay. The proposed approaches are applied in a model with three nodes including the source (D2D user), the relay and the destination (D2D user), the system throughput is investigated in terms of the ergodic capacity and the outage capacity, where the analytical results are obtained approximately. Our numerical results verify the our derivations, and also points out the impact of CCI on system performance. Finally, this investigation provide fundamental design guidelines for selecting hardware of energy harvesting circuits that satisfies the requirements of a practical cooperative D2D system

Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study

Deep learning-based systems have been shown to be vulnerable to adversarial attacks in both digital and physical domains. While feasible, digital attacks have limited applicability in attacking deployed systems, including face recognition systems, where an adversary typically has access to the input and not the transmission channel. In such setting, physical attacks that directly provide a malicious input through the input channel pose a bigger threat. We investigate the feasibility of conducting real-time physical attacks on face recognition systems using adversarial light projections. A setup comprising a commercially available web camera and a projector is used to conduct the attack. The adversary uses a transformation-invariant adversarial pattern generation method to generate a digital adversarial pattern using one or more images of the target available to the adversary. The digital adversarial pattern is then projected onto the adversary's face in the physical domain to either impersonate a target (impersonation) or evade recognition (obfuscation). We conduct preliminary experiments using two open-source and one commercial face recognition system on a pool of 50 subjects. Our experimental results demonstrate the vulnerability of face recognition systems to light projection attacks in both white-box and black-box attack settings.Comment: To appear in the proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR) Biometrics Workshop 2020 - 9 pages, 8 figure

Preliminary Result of Arthroscopic Anterior Cruciate Ligament Reconstruction Using Anterior Half of Peroneus Longus Tendon Autograft

BACKGROUND: Anthroscopic anterior cruciate ligament reconstruction is one of the most successful operations in sports medicine. At present, ligament autografts have been the best method due to good histocompatibility, rapid healing, no cross-contamination, and low cost of treatment. However, autografts do not have infinite amount and are also not always feasible. Anterior half of peroneus longus tenden autograft is likely to become a source of autograft with many advantages. This study aims to evaluate the clinical outcomes of anthroscopic anterior cruciate ligament reconstruction using anterior half of peroneus longus tendon autograft (AHPLT). AIM: To evaluate the initial outcome of ACL reconstruction arthroscopy by anterior half of peroneus longus tendon. METHODS: This is a prospective non-controlled case series. RESULTS: A prospective study on 30 patients (from 9 / 2016 to 01 / 2019) had both ACL and MCL injury who had operated ACL reconstruction using anterior half of peroneus longus tendon autograft (AHPLT) at Department of General Orthopaedic and Trauma, Viet Duc hospital. Our outcome: the year average 35.4 ys, the rate of ACL rupture combined with meniscus injury was 40%. The average diameter AHPLT autograft is 7.0 mm. The function Lysholm scores improved from 59 to 94.27 postoperative 6 months. No difference beetwen the AOFAS scale of preoperative and postoperative. CONCLUSION: Peroneus longus tendon is recommended to be a safe and practical autograft resource for anthroscopic anterior cruciate ligament reconstruction

Unmanned aerial vehicle-aided cooperative regenerative relaying network under various environments

This paper studies a cooperative relay network that comprises an unmanned aerial vehicle (UAV) enabling amplify-and-forward (AF) and power splitting (PS) based energy harvesting. The considered system can be constructed in various environments such as suburban, urban, dense urban, and high-rise urban where the air-to-ground channels are model by a mixture of Rayleigh and Nakagami-m fading. Then, outage probability and ergodic capacity are provided under different environment-based parameters. Optimal PS ratios are also provided under normal and high transmit power regimes. Finally, the accuracy of the analytical results is validated through Monte Carlo methods

Charge transport in polycrystalline graphene : challenges and opportunities

Graphene has attracted significant interest both for exploring fundamental science and for a wide range of technological applications. Chemical vapor deposition (CVD) is currently the only working approach to grow graphene at wafer scale, which is required for industrial applications. Unfortunately, CVD graphene is intrinsically polycrystalline, with pristine graphene grains stitched together by disordered grain boundaries, which can be either a blessing or a curse. On the one hand, grain boundaries are expected to degrade the electrical and mechanical properties of polycrystalline graphene, rendering the material undesirable for many applications. On the other hand, they exhibit an increased chemical reactivity, suggesting their potential application to sensing or as templates for synthesis of one-dimensional materials. Therefore, it is important to gain a deeper understanding of the structure and properties of graphene grain boundaries. Here, we review experimental progress on identification and electrical and chemical characterization of graphene grain boundaries. We use numerical simulations and transport measurements to demonstrate that electrical properties and chemical modification of graphene grain boundaries are strongly correlated. This not only provides guidelines for the improvement of graphene devices, but also opens a new research area of engineering graphene grain boundaries for highly sensitive electro-biochemical devices