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Obstacle detection by multi-sensor fusion of a laser scanner and dept. camera

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Obstacle detection by multi-sensor fusion of a laser scanner...

International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Zainab Saleem Philip Long Saif Huq Marion McAfee Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) Atlantic Technological University Sligo Ireland Department of Mechanical & Industrial Engineering Atlantic Technological University Galway Ireland Dept. of Elec. & Comp. Eng. and Comp. Science Kinsley Engineering Centre York College of Pennsylvania York

Reliable sensor systems are essential to detect and track the human operator in a human-robot collaborative environment. This work proposes a low-cost obstacle warning system by fusing 2D LiDAR and 3D vision data. Some regions of the workspace can be inaccessible to a single sensor, defined as blind spots. Therefore, by using a 2D LiDAR on the robot's base and a vision sensor on top of the workspace, we ensure coverage of the entire workspace of the manipulator. For a more efficient system, first human operator has been detected using an object detection algorithm then the laser points are segmented. Further, to obtain more accurate results, the data from both sensors has been fused using Kalman Filter. This fusion not only provides accurate and fast distance information on the position of a human worker without leaving any blind spots but is also significantly more affordable than the more common 3D LiDAR plus vision approach.

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Large Scale IoT Training using Arduino

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Large Scale IoT Training using Arduino

IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Rajesh Kushalkar Nirmala Venkat Firuza Karmali Nagesh Karmali Sudhakar Kumar Kannan M. Moudgalya G. V. V. Sharma Dept. of Comp. Science & Engg. IIT Bombay Mumbai India Dept. of Chemical Engineering IIT Bombay Mumbai India Dept. of Sys. & Cont. Engg. IIT Bombay Mumbai India Dept. of Chem. Engg. IIT Bombay Mumbai India Dept. of Elec. Engg. IIT Hyderabad Mumbai India

This article explains the self-learning Spoken Tutorials on the popular open source microcontroller Arduino UNO. It introduces freely downloadable books that explain how Arduino UNO can be controlled from Scilab, Xcos, Python, Julia, and OpenModelica. Using these, 1,500 college teachers and 75,000 students have been trained. An Arduino Cloud is created to make this experience available also to those who do not have access to the Arduino hardware.

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UNCERTAINTY AWARE MULTITASK PYRAMID VISION TRANSFORMER FOR UAV-BASED OBJECT RE-IDENTIFICATION

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arXiv 2022年

作者： Ferdous, Syeda Nyma Li, Xin Lyu, Siwei West Virginia University Lane Dept. of Comp. Sci. and Elec. Engr. MorgantownWV26506-6109 United States State University of New York at Buffalo Department of Computer Science and Engineering 317 Davis Hall BuffaloNY14260 United States

Object Re-IDentification (ReID), one of the most significant problems in biometrics and surveillance systems, has been extensively studied by image processing and computer vision communities in the past decades. Learning a robust and discriminative feature representation is a crucial challenge for object ReID. The problem is even more challenging in ReID based on Unmanned Aerial Vehicle (UAV) as the images are characterized by continuously varying camera parameters (e.g., view angle, altitude, etc.) of a flying drone. To address this challenge, multiscale feature representation has been considered to characterize images captured from UAV flying at different altitudes. In this work, we propose a multitask learning approach, which employs a new multiscale architecture without convolution, Pyramid Vision Transformer (PVT), as the backbone for UAV-based object ReID. By uncertainty modeling of intraclass variations, our proposed model can be jointly optimized using both uncertainty-aware object ID and camera ID information. Experimental results are reported on PRAI and VRAI, two ReID data sets from aerial surveillance, to verify the effectiveness of our proposed approach. © 2022, CC BY.

关键词： Cameras

LNGate: Powering IoT with next generation lightning micro-payments using threshold cryptography

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arXiv 2021年

作者： Kurt, Ahmet Mercan, Suat Shlomovits, Omer Erdin, Enes Akkaya, Kemal Dept. of Elec. and Comp. Engineering Florida International University MiamiFL United States ZenGo X Tel Aviv Israel Dept. of Computer Science University of Central Arkansas Conway AR United States

Bitcoin has emerged as a revolutionary payment system with its decentralized ledger concept however it has significant problems such as high transaction fees and long confirmation times. Lightning Network (LN), which was introduced much later, solves most of these problems with an innovative concept called off-chain payments. With this advancement, Bitcoin has become an attractive venue to perform micro-payments which can also be adopted in many IoT applications (e.g. toll payments). Nevertheless, it is not feasible to host LN and Bitcoin on IoT devices due to the storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN through an untrusted gateway node. The gateway hosts LN and Bitcoin nodes and can open & close LN channels, send LN payments on behalf of the IoT device. This delegation approach is powered by a (2,2)-threshold scheme that requires the IoT device and the LN gateway to jointly perform all LN operations which in turn secures both parties’ funds. Specifically, we propose to thresholdize LN’s Bitcoin public and private keys as well as its commitment points. With these and several other protocol level changes, IoT device is protected against revoked state broadcast, collusion, and ransom attacks. We implemented the proposed protocol by changing LN’s source code and thoroughly evaluated its performance using a Raspberry Pi. Our evaluation results show that computational and communication delays associated with the protocol are negligible. To the best of our knowledge, this is the first work that implemented threshold cryptography in LN. © 2021, CC BY-NC-ND.

关键词： Internet of things

An evaluation of cryptocurrency payment channel networks and their privacy implications

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arXiv 2021年

作者： Erdin, Enes Mercan, Suat Akkaya, Kemal Department of Computer Science University of Central Arkansas ConwayAR72035 United States Dept. of Elec. and Comp. Engineering Florida International University MiamiFL33174 United States

Cryptocurrencies redefined how money can be stored and transferred among users. However, independent of the amount being sent, public blockchain-based cryptocurrencies suffer from high transaction waiting times and fees. These drawbacks hinder the wide use of cryptocurrencies by masses. To address these challenges, payment channel network concept is touted as the most viable solution to be used for micro-payments. The idea is exchanging the ownership of money by keeping the state of the accounts locally. The users inform the blockchain rarely, which decreases the load on the blockchain. Specifically, payment channel networks can provide transaction approvals in seconds by charging a nominal fee proportional to the payment amount. Such attraction on payment channel networks inspired many recent studies which focus on how to design them and allocate channels such that the transactions will be secure and efficient. However, as payment channel networks are emerging and reaching large number of users, privacy issues are becoming more relevant that raise concerns about exposing not only individual habits but also businesses' revenues. In this paper, we first propose a categorization of the existing payment networks formed on top of blockchain-backed cryptocurrencies. After discussing several emerging attacks on user/business privacy in these payment channel networks, we qualitatively evaluate them based on a number of privacy metrics that relate to our case. Based on the discussions on the strengths and weaknesses of the approaches, we offer possible directions for research for the future of privacy based payment channel networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

Cryptocurrency solutions to enable micro-payments in consumer iot

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arXiv 2021年

作者： Mercan, Suat Kurt, Ahmet Erdin, Enes Akkaya, Kemal Dept. of Elec. and Comp. Engineering Florida International University MiamiFL33174 United States Department of Computer Science University of Central Arkansas ConwayAR72035 United States

The successful amalgamation of cryptocurrency and consumer Internet of Things (IoT) devices can pave the way for novel applications in machine-to-machine economy. However, the lack of scalability and heavy resource requirements of initial blockchain designs hinders the integration as they prioritized decentralization and security. Numerous solutions have been proposed since the emergence of Bitcoin to achieve this goal. However, none of them seem to dominate and thus it is unclear how consumer devices will be adapting these approaches. Therefore, in this paper, we critically review the existing integration approaches and cryptocurrency designs that strive to enable micro-payments among consumer devices. We identify and discuss solutions under three main categories;direct integration, payment channel network and new cryptocurrency design. The first approach utilizes a full node to interact with the payment system. Offline channel payment is suggested as a second layer solution to solve the scalability issue and enable instant payment with low fee. New designs converge to semi-centralized scheme and focus on lightweight consensus protocol that does not require high computation power which might mean loosening the initial design choices in favor of scalability. We evaluate the pros and cons of each of these approaches and then point out future research challenges. Our goal is to help researchers and practitioners to better focus their efforts to facilitate micro-payment adoptions. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

A Survey of Biometric and Machine Learning Methods for Tracking Students' Attention and Engagement 19

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A Survey of Biometric and Machine Learning Methods for Track...

19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Villa, Maria Gofman, Mikhail Mitra, Sinjini Almadan, Ali Krishnan, Anoop Rattani, Ajita Coco Koda Web Development California State University Dept. of Computer Science United States California State University Dept. of Information Systems and Decision Sciences United States Wichita State University Dept. of Elec. Eng. and Com. Science Wichita United States

ISBN: (纸本)9781728184708

The skills of focusing and paying attention are critical to student learning. According to Piontkowski et al. [40], Educators often talk about attention as a general mental state in which the mind focuses on some special feature of the environment. As such, attention is considered essential for learning. It is hard to believe that the student who disregards instruction will benefit from it. Thus, the teacher needs reliable signs of the student's state of *** is challenging, however, for instructors to ascertain signs of student attention in large classrooms with many students. Additional challenges arise in online classrooms, which often limit instructors to watching students' body language in video feeds, where they cannot see, for example, distractions in the students' environment. Biometrics and machine learning approaches can help instructors evaluate their students' level of attentiveness in both physical and online classrooms and introduce appropriate interventions to improve learning *** the field of automated attention tracking research is steadily amassing new publications, no survey works have charted the progress of research or encouraged new research. We have filled this gap with this survey of salient works that use biometrics and machine learning to track attention. Specifically, we focus on discussions and analyses of methods that use eye gazing, facial movements and expressions, behavioral biometrics such as body movements, analyses of brainwave signals and psychological states, and multimodal biometrics. We conclude with a discussion of promising future research directions with focus on multimodal biometric techniques. © 2020 IEEE.

关键词： Students

Use of the Lévy Flight Firefly Algorithm in 2D McClellan Unconstrained Transform Adaptive Filters 63

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Use of the Lévy Flight Firefly Algorithm in 2D McClellan Un...

63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020

作者： Hussain, M. Jenkins, W.K. Radhakrishnan, C. Pennsylvania State University School of Electrical and Computer Engineering University ParkPA United States University of Illinois Dept. of Elec. and Comp. Engr. Urbana-ChampaignIL United States

ISBN: (纸本)9781538629161

Recently published results demonstrated that bio-inspired Lévy Flight Firefly Algorithms (LFFAs) can be effectively applied to IIR adaptive filter structures, non-linear adaptive structures, adaptive filters using second order coupled form sections, and adaptive lattice-ladder structures which have been well known for many decades. This paper shows how the LFFA can be used in 2D McClellan Unconstrained Transform adaptive filters so the transform domain parameters can be adjusted to accurately approximate the frequency domain contours when no a priori contour information is available. It appears that this is the first time that the LFFA has been used in 2D adaptive filtering. © 2020 IEEE.

关键词： Adaptive filters

A multi-criteria approximation algorithm for influence maximization with probabilistic guarantees

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A multi-criteria approximation algorithm for influence maxim...

2020 Symposium on Algorithm engineering and Experiments, ALENEX 2020

作者： Khan, Maleq Pandurangan, Gopal Pham, Nguyen Dinh Vullikanti, Anil Zhang, Qin Department of Elec. Engg. and Computer Science Texas A&M University-Kingsville TX United States Department of Computer Science University of Houston HoustonTX77204 United States Dept. of Computer Science Biocomplexity Institute University of Virginia CharlottesvilleVA United States Department of Computer Science Indiana University Bloom-ingtonIN United States

ISBN: (纸本)9781611976007

The well-studied influence maximization problem involves choosing a seed set of a given size, which maximizes the expected influence. However, such solutions might have a significant probability of achieving low influence, which might not be suitable in many applications. In this paper, we consider a different approach: find a seed set that maximizes the influence set size with a given probability. We show that this objective is not submodular, and design a greedy, multi-criteria approximation algorithm for this problem with rigorous approximation guarantees. We also evaluate our algorithm on multiple datasets, and show that they have similar or better quality as the ones optimizing the expected influence, but with additional guarantees on the probability. Copyright © 2020 by SIAM

关键词： Approximation algorithms