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Contactless Authentication Device using Palm Vein and Palm Print fusion Biometric Technology for Post Covid World 5

Contactless Authentication Device using Palm Vein and Palm P...

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5th International conference on Design Innovations for 3Cs Compute Communicate Control, ICDI3C 2021

作者： Kala, K.H.S. Kumar, Shreya Reddy, Rakshanda B Shastry, Nishanth Thakur, Rounak MVJ College of Engineering Dept. of Electronics and Communication Bangalore India

ISBN: (纸本)9781665425698

This paper proposes a secure, contactless authentication and biometric identification system incorporating palm vein and palm print image fusion. It acts as a security device without the need to physically touch it. In a world still recuperating from the aftermath of the COVID-19 pandemic, our no-contact device would find particular use in office spaces, educational institutes, etc. The system comprises near-infrared (NIR), light-emitting diode (LED) arrays, a low-cost complementary metal-oxide-semiconductor (CMOS) visual sensor, a 'Raspberry Pi SBC', and other components. In real-time, the palm vein and palm-print images are acquired at the same time with real-time Image Quality Assessment and Correction (IQAC). Vein and palm print features of interest are then made prominent using certain preprocessing algorithms. To reduce image storage requirements, we implement a wavelet-based fusion strategy on the images. Finally, using a deep scattering convolutional network, features are extracted. Although at present, a system uses state vector machines for training and recognition, this article advances the state-of-the-art method by introducing reduced vector machines to biometric authentication applications. Characteristics of some other biometric identification systems are also discussed to highlight features. The operational requirements of our proposed prototype are significantly lesser than fusion strategies employed by earlier technologies. It also proves to be more economical when implemented on a large scale, thus increasing the commercial viability of our system. © 2021 IEEE.

关键词： Biometrics

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A Methodology for Principled Approximation in Visual SLAM 20

A Methodology for Principled Approximation in Visual SLAM

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ACM International conference on Parallel architectures and Compilation Techniques (PACT)

作者： Pei, Yan Biswas, Swarnendu Fussell, Donald S. Pingali, Keshav Univ Texas Austin Austin TX 78712 USA Indian Inst Technol Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9781450380751

This paper proposes a methodology for exploiting approximate computing to reduce the time and energy requirements of Simultaneous Localization and Mapping (SLAM) algorithms, which are used in important problem domains like robotics and autonomous driving in which autonomous agents navigate through unknown environments. algorithms for SLAM use sensors to probe the environment, integrate this information into a map of the surroundings (mapping), and determine where the agent is in this map (localization). Visual SLAM algorithms use cameras as sensors. They can be used in places where GPS information is not available, but they have high computational requirements, leading to poor performance and high energy usage on embedded platforms. Existing studies of approximation in SLAM have mostly used offline control, which requires the trajectory be known before the agent starts to move. This is not realistic in most SLAM applications. In this paper, we present a general methodology for applying principled online approximation to visual SLAM algorithms. We implemented our proposed methodology in four visual SLAM algorithms (including one visual inertial SLAM algorithm) and evaluated them on several platforms. Our experimental results show that across different algorithms and platforms, our proposed methodology results in savings of up to 77% and 40% in computation time and energy consumption respectively with acceptable quality loss in localization and mapping accuracy over a variety of inputs.

关键词： Approximate computing SLAM online control Elasticfusion Kinect-fusion ORB-SLAM2 ICE-BA

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PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-Based Attentive Cont-Conv fusion Module 34

PI-RCNN: An Efficient Multi-Sensor 3D Object Detector with P...

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34th AAAI conference on Artificial Intelligence / 32nd Innovative applications of Artificial Intelligence conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Xie, Liang Xiang, Chao Yu, Zhengxu Xu, Guodong Yang, Zheng Cai, Deng He, Xiaofei Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Fabu Inc Hangzhou Peoples R China Alibaba Zhejiang Univ Joint Inst Frontier Technol Hangzhou Peoples R China

ISBN: (纸本)9781577358350

LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data effectively. However, their fusion methods based on Bird's Eye View (BEV) or voxel format are not accurate. In this paper, we propose a novel fusion approach named Point-based Attentive Cont-conv fusion(PACF) module, which fuses multi-sensor features directly on 3D points. Except for continuous convolution, we additionally add a Point-Pooling and an Attentive Aggregation to make the fused features more expressive. Moreover, based on the PACF module, we propose a 3D multi-sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks. PI-RCNN employs a segmentation sub-network to extract full-resolution semantic feature maps from images and then fuses the multi-sensor features via powerful PACF module. Beneficial from the effectiveness of the PACF module and the expressive semantic features from the segmentation module, PI-RCNN can improve much in 3D object detection. We demonstrate the effectiveness of the PACF module and PI-RCNN on the KITTI 3D Detection benchmark, and our method can achieve state-of-the-art on the metric of 3D AP.

关键词： Object detection

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Angle-Only, Range-Only and Multistatic Tracking Based on GM-PHD Filter

Angle-Only, Range-Only and Multistatic Tracking Based on GM-...

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International conference on Information fusion

作者： Dimitri Hamidi Elad Kevelevitch Prashant Arora Rick Gentile Vincent Pellissier The MathWorks GmbH Munich Germany MathWorks Inc. Natick MA USA

ISBN: (纸本)9781665414272

Multi-object detection and tracking with spatially distributed sensor networks are used in many applications across the domains of autonomous and surveillance systems. The sensors typically used in these systems often provide incomplete observations such as bistatic and angle- or range-only measurements, thus posing a challenge to the task of retrieving the targets and estimating their state. In this paper, we first present a new variant of a multi-sensor tracking algorithm based on the Gaussian-mixture probability hypothesis density (GM-PHD) filter. Next, we show how it can be applied on fusing incomplete observations. For tracking asynchronous range- and angle-only measurements, we leverage the well-known concepts of angle and range parametrization, respectively, to describe the adaptive target birth density based on the parameters of received observations. In the case of multistatic tracking, we propose parametrizing the birth density from target hypotheses, generated by statically fusing bistatic range measurements, using the M-best S-D assignment algorithm. We investigate the performance using challenging simulation scenarios and evaluate it with established tracking metrics. Our preliminary results demonstrate the effectiveness of the proposed algorithms. Furthermore, for range- and angle-only fusion, the more common use case of unsynchronized sensor measurements is supported. While many algorithms in the literature are tailored for a specific problem, we show that the proposed GM-PHD tracker is generic and can be potentially leveraged in a wide range of sensor fusion and tracking applications.

关键词： Adaptation models Target tracking Density measurement Surveillance conferences sensor fusion Filtering algorithms

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Data fusion and State Estimation Using Belief Propagation in Gas Distribution Networks

Data Fusion and State Estimation Using Belief Propagation in...

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International Universities Power Engineering conference (UPEC)

作者： Goekhan Demirel Steven de Jongh Felicitas Mueller Thomas Leibfried Institute of Electrical Energy Systems and High Voltage Technology (IEH) Karlsruhe Institute of Technology Karlsruhe Germany Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

ISBN: (数字)9781665455053

ISBN: (纸本)9781665455060

This paper proposes a solution to the state estimation problem in gas networks using the distributed belief propagation (BP) algorithm. Power system identification applications require precise and robust state estimatiors as well as various sensor information. Compared to augmenting the power system with a very large number of sensors, a limited number of sensors and probabilistic graphical models can be used to infer the system state and reduce hardware investments. A novel BP algorithm propagates the pressure quantities at nodes in the gas network based on pressure manometer signals and applies a correction based on the information of neighboring nodes in the fusion step by using additional supporting sensors. Finally, the data fusion algorithm is demonstrated for a 14-node gas distribution network based on real data. This paper presents a novel algorithm aimed at tackling the traditional weighted least squares method to validate the developed novel approach in order to highlight the advantage of the distributed inference algorithm over traditional methods.

关键词： Power engineering Graphical models Data integration Distribution networks Probabilistic logic Inference algorithms Power systems

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Local filter-based sequential and distributed fusion state estimation for nonlinear multi-sensor systems with asynchronously correlated noises

Local filter-based sequential and distributed fusion state e...

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IEEE International conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Kun Yang Yao Zhang Yang Liu Jun–Tao Liu Kai Zhao Beijing Institute of Astronautical systems Engineering Northwestern Polytechnical University Beijing Institute of Astronautical systems Engineering

ISBN: (纸本)9781665478373

In this paper, sequential and distributed fusion state estimation algorithms based on local filter are proposed for nonlinear multi-sensor systems with asynchronously correlated noises. At first, in order to avoid the computational burden of measurement augmentation in centralized fusion filter, a sequential fusion filter is deduced by correcting measurement noises recursively. Subsequently, considering the problem that the parameters in covariance intersection (CI) fusion algorithm are difficult to estimate and the estimation accuracy of the sequential fast CI (SFCI) fusion algorithm is poor, a novel distributed fusion filter is proposed, where local filters are sent to the fusion center as the measurements and fusion predictor in the fusion center is sent to local filters by feedback. Then, to be suitable for practical applications, the numerical implementation based on third-degree spherical-radial rule is given. Finally, the superiority of the proposed algorithms is shown by using a nonlinear model.

关键词： Software algorithms Software quality Filtering algorithms Prediction algorithms Numerical models Software reliability Noise measurement

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Precise Indoor Positioning System for Mobile Robots via Smoothed UWB/IMU sensor fusion

Precise Indoor Positioning System for Mobile Robots via Smoo...

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International conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Mahmoud Elsanhoury Jyri Nieminen Petri Välisuo Akpojoto Siemuri Janne Koljonen Mohammed Elmusrati Heidi Kuusniemi Digital Economy Platform University of Vaasa Vaasa Finland VEBIC Platform Digital Economy Platform University of Vaasa Vaasa Finland School of Technology and Innovations Digital Economy Platform University of Vaasa Vaasa Finland School of Technology and Innovations University of Vaasa Vaasa Finland Digital Economy Platform School of Technology and Innovations University of Vaasa Vaasa Finland

Indoor positioning systems (IPSs) are the foundations for all indoor location-based services and applications. In this article, we present a precise and robust IPS using ultra wide-band (UWB) technology fused with an inertial measurement unit (IMU). Both technologies are integrated to account for the non-line-of-sight (NLOS) problems arising in a dense challenging environment found within an industrial laboratory in Finland. Besides the conventional estimation techniques e.g. extended Kalman filter (EKF), we employ the Rauch-Tung-Striebel (RTS) smoothing algorithm in addition to a multivariate regression-based offset compensation method to improve the overall positioning accuracy of the system. The recommended number of distributed UWB anchors versus the coverage area is also discussed and tested in this article. The experiments were held by a patrolling mobile robot with millimeter accuracy, which acted as a ground truth reference to all used algorithms. The positioning estimation results showed a superior performance by the proposed method (UWB/IMU EKF-RTS-LR) with mean accuracy of 4.7 cm, and 9.6 cm for more than 95% of the time.

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Simultaneous Localization and Mapping Algorithm based on 3D Laser for Unmanned Aerial Vehicle 4th

Simultaneous Localization and Mapping Algorithm based on 3D ...

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4th International conference on Electrical Engineering and Control applications, ICEECA 2019

作者： Demim, Fethi Nemra, Abdelkrim Mouali, Oumima Hedir, Mehdia Rouigueb, Abdenebi Hamerlain, Mustapha Bendoumi, Mohamed Amine Bazoula, Abdelouahab Laboratoire des Véhicules Autonomes Intelligents Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Faculté des Sciences Mathématiques Physiques et Naturelles Université del Manar Tunis Tunisia Image Processing and Radiation Laboratory Faculty of Electronic and Informatics USTHB Bab Ezzouar Algiers Algeria Data Fusion and Analysis Laboratory Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Centre de Développement des Technologie Avancées CDTA Baba Hassen Algeria

ISBN: (纸本)9789811564024

This paper focuses on developing a robust solution for the Simultaneous Localization and Mapping (SLAM) problem to increase the autonomy of Unmanned Aerial Vehicles (UAV). The investigated topics are related to data fusion, localization, features extraction and matching, map building, 3D pose estimation and SLAM. One important aspect of the autonomous navigation which should be investigated as well as the fusion of data from the different sensor. The data fusion algorithms are very important and their performances are closely dependent on both performances of the constructed map and the accuracy of the UAV position within this map. Optimal and robust filter using centred Gaussian noises is implemented. The original contribution of this work is the proposition and the adaptation of the Extended Kalman Filter (EKF) to solve the Inertial Navigation Systems INS/3D laser UAV navigation problem. Simulation results for 3D flight scenario are presented to demonstrate the advantages of the hybrid localization based on EKF compared with results of the INS relative localization based technique. Good results were obtained with the EKF using noise process and/or measurement noise characteristics, particularly in the case of centred Gaussian noises. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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International conference on Computational Intelligence and Communication Technology, CICT 2019

International Conference on Computational Intelligence and C...

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International conference on Computational Intelligence and Communication Technology, CICT 2019

ISBN: (纸本)9789811512742

The proceedings contain 46 papers. The special focus in this conference is on Computational Intelligence and Communication Technology. The topics include: Mining public opinion on plastic ban in India;whale neuro-fuzzy system for intrusion detection in wireless sensor network;the threat detection framework for securing semantic web services based on xacml;real-time detection of fake account in twitter using machine-learning approach;DPLOOP: Detection and prevention of loopholes in web application security;security enhancement using modified aes and diffie–hellman key exchange;detection of advanced linux malware using machine learning;host-server-based malware detection system for android platforms using machine learning;identification of windows-based malware by dynamic analysis using machine learning algorithm;secure e-mail communications through cryptographic techniques—a study;prediction of compressive strength of ultra-high-performance concrete using machine learning algorithms—sfs and ann;current trends in cryptography, steganography, and metamorphic cryptography: A survey;enhancement in algorithmic design for scalability measurement of web application;a survey on internet of things (iot): Layer-specific, domain-specific and industry-defined architectures;a survey on social networking using concept of evolutionary algorithms and big data analysis;a comparative study of deployment tools from iot perspective;a perspective of missing value imputation approaches;advanced deadline-sensitive scheduling approaches in cloud computing;a survey of text mining approaches, techniques, and tools on discharge summaries;smart environmental monitoring based on iot: Architecture, issues, and challenges;wideband band-stop filter for c-band and s-band applications using finite integration in time-domain technique;classification of plant based on leaf images;role of iot and big data support in healthcare.

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5th International conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2019

5th International Conference on Microelectronics, Electromag...

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5th International conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2019

ISBN: (纸本)9789811538278

The proceedings contain 81 papers. The special focus in this conference is on Microelectronics, Electromagnetics and Telecommunication. The topics include: A Tapered Microstrip-Fed Steering-Shaped Super-Wideband Printed Monopole Antenna;High-Throughput VLSI architectures for VLSI Signal Processing;reform Initiatives for Electrical Distribution Utilities in Jharkhand, India;FPGA Performance Evaluation of Present Cipher Using LCC Key Generation for IoT sensor Nodes;Performance Comparison Analysis of GTTPC and AHTPC Technique for WBAN with Mobile Scenario;Estimation of FSO Link Availability for Visakhapatnam Coastal Region;CPW-Fed Monopole Antenna for RCS Reduction in X-Band applications;Performance Improvement of Solar PV Maximum Power Point Tracking Using Sliding Mode Control Algorithm;surfactant Effect on Bandgap and Crystallite Size of ZnO–TiO2–CeO2 Nanocomposites;TEC Variation Analysis During Near Peak Solar Year Activity Over Indian Subcontinent;a Review on Schemes for Interconnecting Microgrids of Urban Buildings;performance Analysis of Multiwalled Carbon Nanotube, Composite Multiwalled Carbon Nanotube, and Copper-Based Antenna in X-Band applications;wideband Printed Antenna Design Techniques: A Comprehensive Study;comparison of Various algorithms for Side Lobe Level Reduction in 5G Antenna Arrays;analysis of Electromagnetic Shielding Effectiveness Properties of Al6061 Metal Matrix Composites at X-Band for Aerospace applications;a Comprehensive Study and Evaluation of Recommender Systems;design, Simulation and Experimental Validation of Patch Antenna in S-Band Satellite Communication;performance Efficient Floating-Point Multiplication Using Unified Adder–Subtractor-Based Karatsuba Algorithm;Implementation of Program Page, Read Page and Block Erase Operations in NAND Flash Memory Controller;Design of Low Standby Power 10T SRAM Cell with Improved Write Margin;preface.

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