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Optimal wo-way TOA localization and synchronization for moving user devices with clock drift

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arXiv 2020年

作者： Zhao, Sihao Zhang, Xiao-Ping Cui, Xiaowei Lu, Mingquan The Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada The Department of Electronic Engineering Tsinghua University Beijing100084 China The Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position and timing information by round-trip communications to a number of anchor nodes (ANs) at known locations. Compared with the one-way TOA technique, the two-way TOA scheme is easy to implement and has higher localization and synchronization accuracy. Existing two-way TOA methods assume a stationary UD. This will cause uncompensated position and timing errors. In this article, we propose an optimal maximum likelihood (ML) based two-way TOA localization and synchronization method, namely TWLAS. Different from the existing methods, it takes the UD mobility into account to compensate the error caused by the UD motion. We analyze its estimation error and derive the Cramér-Rao lower bound (CRLB). We show that the conventional two-way TOA method is a special case of the TWLAS when the UD is stationary, and the TWLAS has high estimation accuracy than the conventional one-way TOA method. We also derive the estimation error in the case of deviated UD velocity information. Numerical result demonstrates that the estimation accuracy of the new TWLAS for a moving UD reaches CRLB, better than that of the conventional one-way TOA method, and the estimation error caused by the deviated UD velocity information is consistent with the theoretical analysis. © 2020, CC BY-NC-ND.

关键词： Synchronization

Simulation and experimental observation of tunable photonic nanojet and photonic hook upon asymmetric illumination of a mesoscale cylinder with mask

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arXiv 2020年

作者： Minin, Igor V. Minin, Oleg V. Liu, Cheng-Yang Wei, Hao-De Karabchevsky, Alina Tomsk Polytechnic University Tomsk634050 Russia Tomsk State University Tomsk634050 Russia Department of Biomedical Engineering National Yang-Ming University Taipei City Taiwan School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

In this letter, we report on numerical study, fabrication and experimental observations of tunable photonic nanojet and photonic hook. Here, the curved photonic nanojets (photonic hooks) are generated by single mesoscale microcylinder that we fabricated from polydimethylsiloxane (PDMS), upon its boundary illumination and adjustable area at an incident wavelength of λ = 405 nm. Experimental observations were conducted for PDMS microcylinder of diameter d = 5 μm deposited on a silicon substrate with the aluminum mask. Measurements were performed with a scanning optical microscope. Our experimental results are in good agreement with numerical predictions performed with the finite-difference time-domain method. The observed the full widths at half-maximum of photonic hooks are 0.48λ, 0.56λ, and 0.76λ for tilt angles of θ = 0°, 5.7°, and 20.1° respectively, at the mask heights of h = 0, 0.25d and 0.5d, respectively, displaying the specificities of the field localization. These photonic hooks have great potential in complex manipulation such as super-resolution imaging, surface fabrication, and optomechanical manipulation in curved trajectories smaller than *** Codes 65Z05 Copyright © 2020, The Authors. All rights reserved.

关键词： Finite difference time domain method

A secure mist-fog-assisted cooperative offloading framework for sustainable smart city development

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Digital Communications and Networks 2024年

作者： Subhranshu Sekhar Tripathy Sujit Bebortta Mazin Abed Mohammed Muhammet Deveci Haydar Abdulameer Marhoon Radek Martinek School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 India Department of Computer Science Ravenshaw University Cuttack 753003 India Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Telecommunications VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Industrial Engineering Turkish Naval Academy National Defence University 34942 Tuzla Istanbul Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon The Bartlett School of Sustainable Construction University College London 1-19 Torrington Place London WC1E 7HB UK Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Iraq College of Computer Sciences and Information Technology University of Kerbala Karbala Iraq

Practical applications of smart cities and the Internet of Things (IoT) have multiplied, posing many difficulties in network performance, dependability, and security. Concerns of accessibility, reliability, sustainability, and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems. Fog computing offers a foundation for various applications, including cognitive support, health and social services, intelligent transportation systems, and pervasive computing and communications. Fog computing can help enhance these apps' productivity and lower the end-to-end delay experienced by such time-sensitive applications. In this research, we propose a reliable and secure service delivery strategy at the network edge for smart cities. To improve the availability and dependability, along with the security of smart city applications, the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system. Simulation findings suggest a reduction of 40.3% in the delay incurred by each service request for highly dense areas and 60.6% for moderately dense environments. Furthermore, the system has low false-negative rates and high detection and accuracy rates, decreasing service requests 2%.

关键词： Security Reliability Trustworthy computing Resource provisioning Threat detection Fog computing Internet of things

Imbalanced Learning for Cooperative Spectrum Sensing in Cognitive Radio Networks

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Imbalanced Learning for Cooperative Spectrum Sensing in Cogn...

IEEE Global Communications Conference

作者： Lusi Li He Jiang Haibo He Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

We propose a novel cooperative spectrum sensing (CSS) framework for cognitive radio networks based on imbalanced learning techniques, which aims to resolve the skewed category distribution problems of signal data. For a radio channel shared by primary users (PUs) and secondary users (SUs), the signal data composed of energy vectors, in which each energy level is estimated by SU, can be used to detect the channel availability via a classifier. However, due to the nature of this application, the existing category-imbalance problem hinders the detection performance since the trained classifier has a better effect on the dominated category. To enhance the performance, sampling (e.g., oversampling, under-sampling, and combination) algorithms are employed to balance the training data set based on the imbalance degree metric of imbalance-ratio. The balanced training set then can be used to train classifiers with initial parameters, and the validation set can be utilized to tune as well as evaluate the classifiers. In the testing phase, the actual desired performance on unseen signal data can be determined based on the testing set, i.e., whether the channel is available or not. The performance of each sampling algorithm is measured in terms of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). The simulation results demonstrate the effectiveness of our proposed framework compared to traditional CSS methods.

关键词： Cascading style sheets Training Energy states Testing Sensors Training data Fading channels

Design of a stabilisation platform for Parkinson’s disease patient

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Journal of Medical engineering and Technology 2018年第1期42卷 43-51页

作者： Fraiwan, Luay Amir, Safi Ahmed, Fahad Halepota, Junaid Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Biomedical Engineering Department Jordan University of Science and Technology Irbid Jordan

There are an increased number of patients all over the world suffering from postural tremors and rest tremors, the types of tremors associated with Parkinson’s disease and other neurodegenerative diseases such as amyotrophic lateral sclerosis. Currently, there is no cure for such disease and patients have to deal with their condition to continue their life normally with the existing of some helpful instruments. This work presents a self-stabilising Parkinson’s disease (PD) Tray (platform) that can help them to carry objects that they hold with their hand. The proposed design includes a mechanical platform and en electronic system to control the tray and inhibits any vibrations of the base plate of the tray. An algorithm was developed that would take positional data from an Inertia movement sensors IMU, compute angles in degrees from its Euler angle raw data and then use those angles to control three servo motors in a direction counter to the changes in the IMU’s position. The platform, was capable of stabilising the base of the tray such that objects placed in it would not be dropped. The tray was tested on simulating conditions and the result should that the mean absolute value of the acceleration values in X and Y directions were reduced from 2.23 m/sec2 to 0.26 m/sec2 in the X direction and from 1.41 m/sec2 to 0.34 m/sec2 in the Y direction. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Stabilization

Analysis of Negative Capacitance MOSFETs Characteristic with Spacer

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Analysis of Negative Capacitance MOSFETs Characteristic with...

International Symposium on VLSI Technology, Systems and Applications

作者： Yu-Chun Lee Sekhar Reddy Kola Chieh-Yang Chen Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering EECS International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu Taiwan

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable I on /I off ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS.

关键词： Capacitance Iron Semiconductor device modeling MOSFET Integrated circuit modeling Mathematical model Ferroelectric materials

Domain adaptation for robust workload level alignment between sessions and subjects using fNIRS

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arXiv 2020年

作者： Lyu, Boyang Pham, Thao Blaney, Giles Haga, Zachary Sassaroli, Angelo Fantini, Sergio Aeron, Shuchin Tufts University Department of Electrical and Computer Engineering 161 College Avenue MedfordMA02155 United States Tufts University Department of Biomedical Engineering 4 Colby Street MedfordMA02155 United States Tufts University Department of Computer Science 161 College Avenue MedfordMA02155 United States

Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory. Aim: Domain shift in fNIRS data is a challenge in the workload level alignment across different experiment sessions and subjects. In order to address this problem, two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) were used. Approach: Specifically, we used labeled data from one session or one subject to classify trials in another session (within the same subject) or another subject. We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment to fNIRS data acquired during different n-back task levels. We compared these approaches with three supervised methods - multi-class Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). Results: In a sample of six subjects, G-W resulted in an alignment accuracy of 68 ± 4 % (weighted mean ± standard error) for session-by-session alignment, FG-W resulted in an alignment accuracy of 55 ± 2 % for subject-by-subject alignment. In each of these cases, 25 % accuracy represents chance. Alignment accuracy results from both G-W and FG-W are significantly greater than those from SVM, CNN and RNN. We also showed that removal of motion artifacts from the fNIRS data plays an important role in improving alignment performance. Conclusions: Domain adaptation has potential for session-by-session and subject-by-subject alignment of mental workload by using fNIRS data. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks

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arXiv 2020年

作者： Ali Vosoughi, M. Wismüller, Axel Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

Graph topology inference of network processes with co-evolving and interacting time-series is crucial for network studies. Vector autoregressive models (VAR) are popular approaches for topology inference of directed graphs;however, in large networks with short time-series, topology estimation becomes ill-posed. The present paper proposes a novel nonlinearity-preserving topology inference method for directed networks with co-evolving nodal processes that solves the ill-posedness problem. The proposed method, large-scale kernelized Granger causality (lsKGC), uses kernel functions to transform data into a low-dimensional feature space and solves the autoregressive problem in the feature space, then finds the pre-images in the input space to infer the topology. Extensive simulations on synthetic datasets with nonlinear and linear dependencies and known ground-truth demonstrate significant improvement in the Area Under the receiver operating characteristic Curve (AUC) of the receiver operating characteristic for network recovery compared to existing methods. Furthermore, tests on real datasets from a functional magnetic resonance imaging (fMRI) study demonstrate 96.3 percent accuracy in diagnosis tasks of schizophrenia patients, which is the highest in the literature with only brain time-series information. © 2020, CC-BY.

关键词： Directed graphs

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

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arXiv 2021年

作者： Shan, Hongming de Barros Vimieiro, Rodrigo Borges, Lucas Rodrigues da Costa Vieira, Marcelo Andrade Wang, Ge Dept. of Biomedical Engineering Rensselaer Polytechnic Institute New York United States Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Dept. of Medical Imaging Hematology and Clinical Oncology Ribeirão Preto School of Medicine University of São Paulo Ribeirão Preto Brazil Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Digital mammography is still the most common imaging tool for breast cancer screening. Although the benefits of using digital mammography for cancer screening outweigh the risks associated with the x-ray exposure, the radiation dose must be kept as low as possible while maintaining the diagnostic utility of the generated images, thus minimizing patient risks. Many studies investigated the feasibility of dose reduction by restoring low-dose images using deep neural networks. In these cases, choosing the appropriate training database and loss function is crucial and impacts the quality of the results. In this work, a modification of the ResNet architecture, with hierarchical skip connections, is proposed to restore low-dose digital mammography. We compared the restored images to the standard full-dose images. Moreover, we evaluated the performance of several loss functions for this task. For training purposes, we extracted 256,000 image patches from a dataset of 400 images of retrospective clinical mammography exams, where different dose levels were simulated to generate low and standard-dose pairs. To validate the network in a real scenario, a physical anthropomorphic breast phantom was used to acquire real low-dose and standard full-dose images in a commercially avaliable mammography system, which were then processed through our trained model. An analytical restoration model for low-dose digital mammography, previously presented, was used as a benchmark in this work. Objective assessment was performed through the signal-to-noise ratio (SNR) and mean normalized squared error (MNSE), decomposed into residual noise and bias. Results showed that the perceptual loss function (PL4) is able to achieve virtually the same noise levels of a full-dose acquisition, while resulting in smaller signal bias compared to other loss functions. The source code for our deep neural network is available at https://***/WANG-AXIS/LdDMDenoising. © 2021, CC BY.

关键词： Image reconstruction