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12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015

作者： Alinia, Parastoo Saeedi, Ramyar Mortazavi, Bobak Rokni, Ali Ghasemzadeh, Hassan Embedded and Pervasive Systems Lab School of Electrical Engineering and Computer Science Washington State University PullmanWA99164-2752 United States Center for Outcomes Research and Evaluation Yale University New HavenCT06511 United States

ISBN: (纸本)9781467372015

Metabolic equivalent of task (MET) indicates the intensity of physical activities. This measurement is used in providing physical activity intervention in many chronic illnesses such as coronary heart disease, type-2 diabetes, and cancer. Due to the small size, portability, low power consumption, and low cost, wearable motion sensors are widely used to estimate MET values. However, one major obstacle in widespread adoption of current wearable monitoring systems is that the sensors must be worn on predefined locations on the body. This imposes much discomfort for users as they are not allowed to wear the sensors on their own desired body locations. In addition, non-adherence to the predefined location of the sensors results in significant reduction in the accuracy of physical activity monitoring. In this paper, we propose a framework for sensor location-independent MET estimation. We introduce a sensor localization approach that allows users to wear the sensors on different body locations without having to adhere to a specific installation protocol. We study how such an algorithm impacts the performance of MET estimation algorithms. Using daily physical activity data, we demonstrate that an automatic sensor localization algorithm decreases the estimation error of the MET calculation by a factor of 2.3 compared to the case without sensor localization. Furthermore, our sensor localization algorithm achieves an accuracy of 90.8% in detecting on-body locations of wearable sensors. The integration of sensor localization and MET estimation achieves an accuracy of 80% in calculating the MET values of daily physical activities. © 2015 IEEE.

关键词： Location

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Character spotting and autonomous tagging: offline handwriting recognition for Bangla, Korean and other alphabetic scripts

Character spotting and autonomous tagging: offline handwriti...

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作者： Majid, Nishatul Smith, Elisa H. Barney Department of Physics and Engineering Fort Lewis College DurangoCO United States Department of Electrical and Computer Engineering Boise State University BoiseID United States Embedded Internet Systems Lab Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This paper demonstrates a framework for offline handwriting recognition using character spotting and autonomous tagging which works for any alphabetic script. Character spotting builds on the idea of object detection to find character elements in unsegmented word images. An autonomous tagging approach is introduced which automates the production of a character image training set by estimating character locations in a word based on typical character size. Although scripts can vary vividly from each other, our proposed approach provides a simple and powerful workflow for unconstrained offline recognition that should work for any alphabetic script with few adjustments. Here we demonstrate this approach with handwritten Bangla, obtaining a character recognition accuracy (CRA) of 94.8% and 91.12% with precision and autonomous tagging, respectively. Furthermore, we explained how character spotting and autonomous tagging can be implemented for other alphabetic scripts. We demonstrated that with handwritten Hangul/Korean obtaining a Jamo recognition accuracy (JRA) of 93.16% using a tiny fraction of the PE92 training set. The combination of character spotting and autonomous tagging takes away one of the biggest frustrations—data annotation by hand, and thus, we believe this has the potential to revolutionize the growth of offline recognition development. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Character recognition

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A mining technique using n-grams and motion transcripts for body sensor network data repository 10

A mining technique using n-grams and motion transcripts for ...

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Wireless Health 2010

作者： Loseu, Vitali Ghasemzadeh, Hassan Khan, Latifur R. Jafari, Roozbeh Embedded Systems and Signal Processing Lab. Department of Electrical Engineering University of Texas at Dallas Richardson TX 75080-3021 United States Department of Computer Science University of Texas at Dallas Richardson TX 75080-3021 United States

ISBN: (纸本)9781605589893

Recent years have seen a large influx of applications in the field of Body Sensor Networks (BSN). BSN, and in general wearable computers with sensors, can give researchers, users or clinicians access to tremendously valuable information extracted from data that were collected in users' natural environment. With this information, one can monitor the progression of a disease, identify its early onset or simply assess user's wellness. One major obstacle is managing repositories that store large amounts of BSN data. To address this issue, we propose a data mining approach for large BSN data repositories. We represent sensor readings with motion transcripts that maintain structural properties of the signal. To further take advantage of the signal's structure, we define a data mining technique using n-grams. We reduce overwhelmingly large number of n-grams via information gain (IG) feature selection. We report the effectiveness of our approach in terms of the speed of mining while maintaining an acceptable accuracy in terms of precision and recall. We demonstrate that the system can achieve average 99% precision with an average 100% recall on our pilot data with the help of only one transition for each movement. © 2010 ACM.

关键词： Data mining

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Competition: Low-Power Wireless Bus Baseline 19

Competition: Low-Power Wireless Bus Baseline

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Proceedings of the 2019 International Conference on embedded Wireless systems and Networks

作者： Fabian Mager Romain Jacob Reto Da Forno Marco Zimmerling Networked Embedded Systems Lab TU Dresden Germany Computer Engineering and Networks Laboratory ETH Zurich Switzerland

No abstract available.

ISBN: (纸本)9780994988638

No abstract available.

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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Data aware, low cost error correction for wireless sensor networks

Data aware, low cost error correction for wireless sensor ne...

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IEEE Conference on Wireless Communications and Networking

作者： S. Mukhopadhyay D. Panigrahi S. Dey Mobile Embedded Systems Design and Test Lab Department of Electrical and Computer Engineering University of California San Diego USA

One or the main challenges in adoption and deployment of wireless networked sensing applications is ensuring reliable sensor data collection and aggregation, while satisfying the low-cost, low-energy operating constraints of such applications. A wireless sensor network is inherently vulnerable to different sources of unreliability resulting in transient failures. Existing reliability techniques that address transient failures in circuits and communication channels incur prohibitively high energy, bandwidth and cost overheads in the sensor nodes. In this paper we investigate application-level error correction techniques for sensor networks that exploit the properties of sensor data to eliminate any overhead on the sensor nodes, at the expense of nominal buffer requirements at the data aggregator nodes, which are much less cost/energy constrained. Our approach involves use of spatio-temporal correlations in sensor data, the goals of the application, and its vulnerability to various errors. We present our error-correction algorithm and evaluate it through simulations using real and synthetic sensor data. Experimental results validate the feasibility of our approach to provide high degree of reliability in sensor data aggregation, without imposing overheads on sensor nodes.

关键词： Costs Error correction Wireless sensor networks Telecommunication network reliability Power system transients Crosstalk Circuits Application software Communication channels Monitoring

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Adaptive admission control of multimedia traffic in high-speed networks

Adaptive admission control of multimedia traffic in high-spe...

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Proceedings of the 2002 IEEE International Symposium on Intelligent Control

作者： Jagannathan, S. Tohmaz, A. Chronopoulos, Anthony Cheung, H.G. Embedded Systems and Networking Lab. Dept. of Electrical and Comp. Engg The University of Missouri-Rolla 1870 Miner Circle Rolla MO 65409 United States Parallel and Distributed Systems Lab Dept. of Computer Science The University of Texas-San Antonio 6900 North Loop 1604 West San Antonio TX 78249 United States

This paper proposes a novel real-time adaptive admission control (AAC) scheme with a desired quality of service (QoS) guarantee and high network utilization in high-speed networks. The QoS is given in terms of service delay, which is defined as the time it takes for a source to get admitted into the network after it initiates its intended request, packet/cell losses, and transmission delay (time taken to complete transmission from its initiation). AAC uses the following information- the available capacity from a novel adaptive bandwidth estimation scheme, a congestion indicator derived from a congestion controller, Peak Cell Rate (PCR) estimate from new sources, along with the desired QoS metrics, and outputs an 'admit' or 'reject' decision signal to the new sources while guaranteeing QoS and network utilization. Simulation results are presented by streaming ON/OFF and video data into the network. Results show that the proposed AAC admits significantly more traffic compared to other available admission control schemes thereby guaranteeing high network utilization while maintaining the desired QoS.

关键词： Adaptive control systems

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Poster Abstract: Improving RGB-D SLAM Using Wi-Fi

Poster Abstract: Improving RGB-D SLAM Using Wi-Fi

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International Symposium on Information Processing in Sensor Networks (IPSN)

作者： Zakieh Sadat Hashemifar Charuvahan Adhivarahan Karthik Dantu Distributed Robotics and Networked Embedded Systems Lab (DRONES) Computer Science and Engineering University at Buffalo Buffalo New York

ISBN: (纸本)9781467391474

Simultaneous Localization and Mapping (SLAM) is the process of learning about both the environment and about a robot's location with respect to the environment and is essential for robots to autonomously navigate. A variety of algorithms using many different sensors such as RGB-D cameras, laser range finders, ultrasonic sensors and others have been proposed to perform SLAM. However, these algorithms face common challenges are that of computational complexity, wrong loop closure detection and failure to localize correctly when robot loses state (kidnapped robot problem). In this work, we utilize Wi-Fi signal strength sensing to aid the SLAM process in indoor environments and address the challenges mentioned above.

关键词： Simultaneous localization and mapping Wireless fidelity Indoor environments Lighting Feature extraction

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Automated customer segmentation based on smart meter data with temperature and daylight sensitivity

Automated customer segmentation based on smart meter data wi...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Christian Beckel Leyna Sadamori Silvia Santini Thorsten Staake Dept. of Computer Science ETH Zurich Switzerland Embedded Systems Lab TU Dresden Germany Energy Efficient Systems University of Bamberg Germany

Utilities increasingly leverage knowledge on their customer's household characteristics in their energy efficiency programs. Examples of such characteristics include the number of persons per household, their employment status, or the type of dwelling they live in. This information allows utilities to personalize energy efficiency campaigns, which increases participation rates and in turn leads to larger energy savings and higher customer retention. However, gathering this information through surveys is costly and cumbersome. We therefore investigate the possibility to automatically infer household characteristics from electricity consumption data measured by an off-the-shelf smart meter. In this paper, we develop a method to determine the sensitivity of a household to outdoor temperature and the times of sunset/sunrise, and use this information to improve the performance of our household classification system. We further investigate the relevance of different features for such a system. Our evaluation is based on smart meter data collected at a 30-minute granularity in more than 4000 Irish households over a period of 75 weeks. The results show that we can improve accuracy by up to 2.3 percentage points using temperature and daylight coefficients. The characteristics floor area, type of dwelling, and percentage of installed energy-efficient light bulbs particularly benefit from temperature and daylight coefficients. Finally, we investigate the impact of the data granularity on the classification performance and show that semi-hourly or hourly data is required, as it performs on average 6.6 percentage points better than using daily consumption averages.

关键词： Temperature sensors Resistance heating Meteorology Smart meters Temperature distribution

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