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IEEE Workshop on Advances in Information, Electronic and electrical engineering (AIEEE)

作者： Anatolijs Zabasta Nadezda Kunicina Kristaps Vitols Ilmars Duritis Uldis Grunde Janis Judvaitis Modris Greitans Ilga Sematovica Aija Malniece Ilja Galkins Institute of Electrical Engineering and Electronics EEF Riga Technical University Riga Latvia Faculty of Veterinary Medicine Latvia University of Life Sciences and Technologies Jelgava Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia

High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of productivity and reduced longevity. The aim of this work is to develop a reticulo-ruminal long-acting cyber-physical system for monitoring rumen parameters. Three scientific institutions in cooperation with two farmers conduct the research in order to create a prototype of a low-power wireless sensor network system for early diagnostic of subacute rumen acidosis of dairy cows. The new system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes a base station for data collection from boluses, an MQTT broker, a web server and a database. Data communication solution has been developed and tested in the laboratory, and micro-controllers have been selected and adapted for data processing. In addition, research is under way to create an autonomous long-term power supply system. Work shall be conducted in two directions: (a) stand-alone battery-powered electricity supply system; (b) an autonomous power supply system based on the generation of an electrostatic generator. The results of the initial stage of the research are discussed in this paper.

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Frequency comb distillation for optical superchannel transmission

arXiv

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arXiv 2021年

作者： Prayoonyong, Chawaphon Boes, Andreas Xu, Xingyuan Tan, Mengxi Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Corcoran, Bill Photonic Communications Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Optical Sciences Centre Swinburne University Hawthorne VIC3122 Australia Centre for Micro-Photonics Swinburne University Hawthorne VIC3122 Australia Department of Physics City University of Hong Kong Tat Chee Ave Hong Kong Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an China INRS – EMT VarennesQCJ3X 1S2 Canada Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China

Optical frequency combs can potentially provide an efficient light source for multi-terabit-per-second optical superchannels. However, as the bandwidth of these multiwavelength light sources is increased, it can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this, we demonstrate wideband noise reduction for comb lines using a high-Q microring resonator whose resonances align with the comb lines, providing tight optical filtering of multiple combs lines at the same time. By distilling an optical frequency comb in this way, we are able to reduce the required comb line OSNR when these lines are used in a coherent optical communications system. Through performance tests on a 19.45-GHz-spaced comb generating 71 lines, using 18 Gbaud, 64-QAM sub-channels at a spectral efficiency of 10.6 b/s/Hz, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side. This demonstration provides a method to enable low power optical frequency combs to be able to support high bandwidth and high-capacity communications. © 2021, CC BY.

关键词： Natural frequencies

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Optical frequency microcomb distillation for super channel data transmission

TechRxiv

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TechRxiv 2021年

作者： Prayoonyong, Chawaphon Boes, Andreas Xu, Xingyuan Tan, Mengxi Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Corcoran, Bill Photonic Communications Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Optical Sciences Centre Swinburne University Hawthorne VIC3122 Australia Centre for Micro-Photonics Swinburne University Hawthorne VIC3122 Australia Department of Physics City University of Hong Kong Tat Chee Ave Hong Kong Xi’an Institute of Optics and Precision Mechanics The Chinese Academy of Sciences Xi’an China INRS – EMT VarennesQCJ3X 1S2 Canada Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China

Microcombs provide a potential compact and efficient light source for multi-Terabit-per-second optical superchannels. However, as the bandwidth of these multiwavelength light sources is increased, this can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this issue, we propose wideband noise reduction for comb lines using a high-Q microring resonator, whose resonances align with comb lines. When applying the proposed distillation to a superchannel system with 18 Gbaud, 64-QAM sub-channels in a > 10 Tb/s optical superchannel, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side. © 2021, CC BY.

关键词： Distillation

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Towards explanations of plan execution for human-robot teaming 1

Towards explanations of plan execution for human-robot teami...

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Joint 1st International Workshop on the Semantic Descriptor, Semantic Modeling and Mapping for Humanlike Perception and Navigation of Mobile Robots toward Large Scale Long-Term Autonomy and the 3rd International Workshop on the Applications of Knowledge Representation and Semantic Technologies in robotics, SDMM 2019 and AnSWeR 2019

作者： Moon, Jiyoun Magazzeni, Daniele Cashmore, Michael Buksz, Dorian Lee, Beom-Hee Moon, Yong-Seon Roh, Sang-Hyun Automation and Systems Research Institute Department of Electrical and Computer Engineering Seoul National University Korea Republic of King’s College London LondonWC2R 2LS United Kingdom University of Strathclyde GlasgowG1 1XH United Kingdom Department of Electronics Engineering Sunchon National University Korea Republic of REDONE TECHNOLOGIES CO. LTD Korea Republic of

Human-robot teaming is inevitable in various applications ranging from manufacturing to field robotics because of the advantages of adaptability and high flexibility. To become an effective team, knowledge regarding plan execution needs to be shared by verbalization. In this respect, semantic scene understanding in natural language is one of the most fundamental components for information sharing between humans and heterogeneous robots, as robots can perceive the surrounding environment in a form that both humans and other robots can understand. In this paper, we introduce semantic scene understanding methods for verbalization of plan execution. We generate sentences and scene graphs, which is a natural language grounded graph over the detected objects and their relationships, with the graph map generated using a robot mapping algorithm. Experiments were performed to verify the effectiveness of the proposed methods. 2019 by the paper’s authors.

关键词： Robots

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Wide-angle giant photonic spin Hall effect

arXiv

引用

arXiv 2022年

作者： Chen, Zhihao Chen, Yu Wu, Yaodong Zhou, Xinxing Sun, Handong Low, Tony Chen, Hongsheng Lin, Xiao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications School of Physics and Electronics Hunan Normal University Changsha410081 China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province Institute of Microscale Optoelectronics Shenzhen University Shenzhen518060 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China

Photonic spin Hall effect is a manifestation of spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e. δ/λ -1, λ is the wavelength), which impedes its precise measurement. To-date proposals to generate giant spin Hall effect (namely with δ/λ > 102) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of ∆0 70◦ by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between s and p polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Polarization

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A Hybrid Approach for Weather-based Power Interruption Forecasting using Multilayer Perceptrons and Parametric Models

A Hybrid Approach for Weather-based Power Interruption Forec...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Longfei Wei Xiaolei Yang Guangyi Liu Renchang Dai Aditya Sundararajan Arif I. Sarwat Global Energy Interconnection Research Institute North America San Jose CA USA State Grid Sichuan Electric Power Company Chengdu Sichuan China Power and Energy Systems Group Oak Ridge National Laboratory Oak Ridge TN USA Department of Electrical & Computer Engineering Florida International University Miami FL USA

ISBN: (数字)9781728157481

ISBN: (纸本)9781728157498

This paper investigates the impact of various weather conditions on the reliability performance of power distribution networks. Especially, a hybrid approach based on multilayer perceptrons (MLPs) and parametric models is proposed to forecast the daily numbers of sustained and momentary power interruptions in the distribution management area using chronological weather data. First, the parametric regression models are implemented to analyze the relationship between power interruptions and different weather characteristics including temperature, wind speed, rain precipitation, air pressure, and lightning. The selected weather characteristics and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to forecast the daily numbers of power interruptions. In addition, a modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated forecasting model. Finally, the real power interruption data collected from a Florida electric utility is used to verify the applicability and effectiveness of the proposed hybrid approach.

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Deep radiomics for brain tumor detection and classification from multi-sequence mri

arXiv

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arXiv 2019年

作者： Banerjee, Subhashis Mitra, Sushmita Masulli, Francesco Rovetta, Stefano Indian Statistical Institute Machine Intelligence Unit Kolkata700108 India University of Calcutta Department of Computer Science and Engineering Kolkata700106 India University of Genova Dept of Informatics Bioengineering Robotics and Systems Engineering Genoa16146 Italy

Glioma constitutes 80% of malignant primary brain tumors in adults, and is usually classified as High Grade Glioma (HGG) and Low Grade Glioma (LGG). The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore, development of automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we thoroughly investigate the power of Deep Convolutional Neural Networks (ConvNets) for classification of brain tumors using multi-sequence MR images. We propose novel ConvNet models, which are trained from scratch, on MRI patches, slices, and multi-planar volumetric slices. The suitability of transfer learning for the task is next studied by applying two existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset, through fine-tuning of the last few layers. Leave-one-patient-out (LOPO) testing, and testing on the holdout dataset are used to evaluate the performance of the ConvNets. Results demonstrate that the proposed ConvNets achieve better accuracy in all cases where the model is trained on the multi-planar volumetric dataset. Unlike conventional models, it obtains a testing accuracy of 95% for the low/high grade glioma classification problem. A score of 97% is generated for classification of LGG with/without 1p/19q codeletion, without any additional effort towards extraction and selection of features. We study the properties of self-learned kernels/ filters in different layers, through visualization of the intermediate layer outputs. We also compare the results with that of state-of-the-art methods, demonstrating a maximum improvement of 7% on the grading performance of ConvNets and 9% on the predi

关键词： Magnetic resonance imaging

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Kolmogorov Model for Large Millimeter-Wave Antenna Arrays: Learning-based Beam-Alignment

Kolmogorov Model for Large Millimeter-Wave Antenna Arrays: L...

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Asilomar Conference on Signals, systems & computers

作者： Wai Ming Chan Hadi Ghauch Taejoon Kim Elisabeth De Carvalho Gabor Fodor Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Digital Communications Telecom Paris Paris France Department of Electrical Engineering and Computer Science The University of Kansas Lawrence KS USA Department of Electronic Systems Aalborg University Denmark School of Electrical Engineering KTH Royal Institute of Technology and Ericsson Research Kista Sweden

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

A new approach is presented to the problem of beam alignment for large-dimensional millimeter-wave antenna systems, with a single radio-frequency chain, based on the application of the Kolmogorov Model (KM) framework to enable learning-based beam alignment. Unlike the conventional exhaustive search-based approach, the proposed KM does not require the entire beam space search, i.e., the number of beam soundings can be extremely smaller than the conventional approach, which is achieved by exploiting the predictive power of KM. We show, across several metrics, that by just sounding 25% of the beams, the proposed method approaches the performance of the exhaustive search method. Simulation results that validate the training and test performance of KM and illustrate the new method with significantly reduced overhead are presented.

关键词： Signal to noise ratio Training Estimation Predictive models Mathematical model Indexes MIMO communication

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Electrophoretic Deposition of 10B Nano/Micro particles in deep silicon trenches for the fabrication of solid state thermal neutron detectors

Electrophoretic Deposition of 10B Nano/Micro particles in de...

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作者： Koirala, Machhindra Wu, Jia Woei Weltz, Adam Dahal, Rajendra Danon, Yaron Bhat, Ishwara Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We present a cost effective and scalable approach to fabricate solid state thermal neutron detectors. Electrophoretic deposition technique is used to fill deep silicon trenches with 10B nanoparticles instead of conventional chemical vapor deposition process. Deep silicon trenches with width of 5-6 μm and depth of 60-65 μm were fabricated in a p-type Si (110) wafer using wet chemical etching method instead of DRIE method. These silicon trenches were converted into continuous p-n junction by the standard phosphorus diffusion process. 10B micro/nano particle suspension in ethyl alcohol was used for electrophoretic deposition of particles in deep trenches and iodine was used to change the zeta potential of the particles. The measured effective boron nanoparticles density inside the trenches was estimated to be 0.7 gm cm-3. Under the self-biased condition, the fabricated device showed the intrinsic thermal neutron detection efficiency of 20.9% for a 2.5 × 2.5 mm2 device area. © 2018 World Scientific Publishing Company.

关键词： Silicon wafers

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A New CNN Occlusion Masking Method for IRT Imaging in Neurosurgery

A New CNN Occlusion Masking Method for IRT Imaging in Neuros...

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European Conference on Circuit Theory and Design, ECCTD

作者： Yahya Moshaei-Nezhad Juliane Müller Christian Schnabel Matthias Kirsch Ronald Tetzlaff Institute of Circuits and Systems Faculty of Electrical and Computer Engineering TU Dresden Dresden Germany Carl Gustav Carus Faculty of Medicine Anesthesiology and Intensive Care Medicine Clinical Sensoring and Monitoring TU Dresden Dresden Germany Carl Gustav Carus Faculty of Medicine TU Dresden Dresden Germany Department of Neurosurgery Asklepios Kliniken Schildautal Seesen Germany

ISBN: (数字)9781728171838

ISBN: (纸本)9781728171845

In this paper, a new occlusion masking method based on Cellular Nonlinear Networks (CNN) for a 2-dimensional (2D) InfraRed Thermography (IRT) brain image is proposed. IRT imaging is one of the noteworthy technique in the medical analysis due to capturing those activities which cannot be seen with unarmed eyes. In neurosurgery, the image occlusion can be classified roughly into two fashions namely, surgery tools and reflection of surgery tools (or cover by the ice-cold saline solution) over the brain cortex surface. In the proposed method, firstly, a CNN texture segmentation is carried out to separate the image textures by the local ratio of the black and white pixels. Thereafter, a CNN dilation is employed to increase the black and white pixels separation. Next, a CNN remove-small-object and filling-holes are exerted for revamping the texture segmentation. Afterwards, the obtained mask is applied to remove the occluded area in the image. To overcome the issues related to reconstruction after masking the image, a solution is given by a 5×5 overlapping sliding window with respect to a reference image, in order to substitute the masked area (pixel values) in the target image. Our proposed method is evaluated on real IRT brain cortex images and compared with other methods. The proposed method shows promising results for IRT brain images in comparison to those other mentioned methods.

关键词： Image segmentation Image reconstruction Surgery Imaging Brain Tools Image texture

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