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Conference on Lasers and Electro-Optics (CLEO)

作者： J. Cheng I. Komissarov G. Chen D. Chakraborty S. Kutcher H. Chen S. Trivedi Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA Brimrose Technology Corp Sparks Maryland USA

We developed a dual optical/x-ray ultrafast photodetector based on in-house grown Cd 0.97 Mg 0.03 Te single crystals. The detector is characterized by ~200 ps full-width-at-half-maximum, readout-electronics limited p... 详细信息

ISBN: (纸本)9781957171258

关键词： Femtosecond lasers Photodetectors Photon counting Pump probe spectroscopy Radiation detectors Ti:sapphire lasers

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New Frontiers for Wave engineering Using Metamaterials

New Frontiers for Wave Engineering Using Metamaterials

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2022年全国微波毫米波会议

作者： Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York Physics Program Graduate Center City University of New York Department of Electrical and Computer Engineering City College of New York

Metamaterials are engineered materials with properties that go well beyond what offered by nature, providing unprecedented opportunities to tailor and enhance the control of waves. In this talk, I discuss our recent activity at microwaves and THz frequencies, showing how suitably tailored meta-atoms and their arrangements open exciting avenues for wave manipulation, including metasurfaces with enhanced wavefront manipulation, and magnet-free nonreciprocity and topological phenomena. Insights into the underlying physics and new devices based on these concepts will be presented.

关键词： New Frontiers for Wave engineering Using Metamaterials

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

TechRxiv

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TechRxiv 2023年

作者： Petrenko, Sasha Hier, Daniel B. Obafemi-Ajayi, Tayo Bone, Mary Timpson, Erik J. Speight, Michael Marsh, William E. Wunsch, Donald C. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Department of Neurology and Rehabilitation University of Illinois at Chicago ChicagoIL United States University of Southeastern Norway Kongsberg Norway Engineering Program Missouri State University SpringfieldMO United States Honeywell Federal Manufacturing & Technologies Kansas CityMO United States

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multicategorical symbolic data. This article proposes Symbolic Tree Adaptive Resonance Theory (START), with additional supervised, Dual-Vigilance (DV-START), and Distributed Dual-Vigilance (DDV-START) formulations, for the clustering of multicategorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot-Marie-Tooth disease using genomic, phenotypic, and proteomic data. © 2023, CC BY.

关键词： Ontology

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A Distributed Task Allocation Protocol for Cooperative Multi-UAV Search and Rescue Systems

A Distributed Task Allocation Protocol for Cooperative Multi...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： De Freitas, Edison Pignaton Basso, Maik Da Silva, Antonio Arlis Santos Vizzotto, Marcos Rodrigues Correa, Mateus Schein Cavalheiro Federal University of Rio Grande Do sul Graduate Program in Electrical Engineering Porto Alegre Brazil Federal University of Rio Grande Do sul Graduate Program in Computer Science Porto Alegre91.501-970 Brazil Federal University of Rio Grande Do sul Department of Electric Automation and Energy Porto Alegre Brazil

ISBN: (纸本)9780738131153

Nowadays, applications involving multiple robotic systems connected through a wireless network - to perform tasks together - have an increased research interest. Unmanned Aerial Vehicles (UAVs) are excellent tools for evaluating and surveying distributed robotic systems applications due to their flexibility and three-dimensional mobility capability. Cooperative systems composed of multiples UAVs may have a centralized coordination which assists the members of a team to make their decisions in a distributed manner regarding the tasks to be performed. This paper proposes a system composed of embedded hardware and a high-level communication protocol that serves as a basis for identifying, distributing, and allocating tasks in a simple and distributed way to be used by groups of UAVs. The proposal is evaluated in a search and rescue scenario. The setup is configured with two types of UAVs - namely, searchers (search for new tasks) and workers (execution of tasks). Also, a set of nine simulation experiments covering three different setups were performed. Then the proposal is evaluated by two sets of field experiments with real vehicles. The obtained results in the proposed experiments demonstrate that the system can be successfully used in the proposed scenario for task distribution and allocation. © 2021 IEEE.

关键词： Antennas

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Tracing the emergence of gendered language in childhood 42

Tracing the emergence of gendered language in childhood

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42nd Annual Meeting of the Cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Prystawski, Ben Grant, Erin Nematzadeh, Aida Lee, Spike W.S. Stevenson, Suzanne Xu, Yang Computer Science and Cognitive Science Programs University of Toronto Canada Department of Electrical Engineering & Computer Sciences University of California Berkeley United States DeepMind Rotman School of Management Department of Psychology University of Toronto Canada Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Are gender associations in general language reflected in the words spoken to and by children? Previous work has suggested that language reveals gender differences in discourse, speech style, language use and acquisition. Work in artificial intelligence has shown that word embeddings trained on large corpora reflect human gender associations. We connect this work to developmental psychology by exploring whether gender associations in word embeddings are present in the linguistic input and output of children, and if so, how early gendered language emerges. We present a computational method that quantifies the gender associations of words and use a corpus of child-caretaker speech to show that these gender associations correlate significantly with those in word embeddings. We discover that gendered word use emerges in English-speaking children around age 2, and the gender associations cannot be explained solely by variables including word length, frequency, concreteness, and valence. © 2020 The Author(s)

关键词： Embeddings

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High Spatial Resolution Terahertz Time-Domain Imaging of Murine Pancreatic Ductal Adenocarcinoma Tissues

High Spatial Resolution Terahertz Time-Domain Imaging of Mur...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Surgery University of Rochester Medical Center Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA

We developed a terahertz time-domain spectroscopy system to generate high-resolution 2-dimensional images of paraffin-embedded murine pancreatic ductal adenocarcinoma tissues using the refractive index and absorption ... 详细信息

ISBN: (纸本)9781957171258

关键词： Absorption coefficient Biological imaging Refractive index Spatial resolution Terahertz spectroscopy Tissue

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Pulsed Terahertz Time Domain Spectroscopy for Evaluating Treatment Efficacy: Initial Validation in Monitoring Pancreatic Ductal Adenocarcinoma

Pulsed Terahertz Time Domain Spectroscopy for Evaluating Tre...

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International Conference on Infrared and Millimeter Waves

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott A. Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Surgery University of Rochester Medical Center Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA

Pancreatic Ductal Adenocarcinoma (PDAC) is a highly invasive malignancy with limited curative effectiveness. Here, we report the viability of using pulsed Terahertz (THz) time domain spectroscopy (THz TDS) to monitor the efficacy of Stereotactic Body Radiation Therapy (SBRT) in treating PDAC in murine models. Our study shows that THz imaging can provide reliable and reproducible results to differentiate the changes in untreated and SBRT-treated PDAC tissue using THz refractive index and absorption coefficient as mapping parameters with statistical significance. Hence the study highlights that pulsed THz-TDS can be used as a technique to monitor tumor responses to immunotherapy, providing a comprehensive assessment of the tumor’s reaction to the treatment.

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Residual block Convolutional Auto Encoder in Content-Based Medical Image Retrieval

Residual block Convolutional Auto Encoder in Content-Based M...

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IEEE IVMSP Workshop

作者： Zahra Tabatabaei Adriá n Colomer Kjersti Engan Javier Oliver Valery Naranjo Dept. of Artificial Intelligence Tyris Tech S.L. Valencia Spain Institute of Research and Innovation in Bioengineering Universitat Polit&#x00E9 cnica de Val&#x00E9 ncia Valencia Spain Department of Electrical Engineering and Computer Science University of Stavanger Norway

Approximately 12 percent of men will suffer from prostate cancer during their lifetime. While some types of this cancer grow slowly and need minimal treatment, others are aggressive and spread quickly. In order to be able to provide a fast and accurate diagnosis, computer vision techniques are widely used to help pathologists in the diagnosis and prognosis tasks. In this work, a new Content-Based Medical Image Retrieval (CBMIR) method is presented. The method, which is named ResCAE, presents a modified Convolutional Auto-Encoder (CAE) with a residual block and a skip layer to extract the relevant features of prostate cancer in Whole Slide Images (WSIs) in SICAPv2 data set. To the best of the author’s knowledge, this is the first study of using a Res-CAE to perform a search engine in prostate cancer images. After extracting features, Euclidean and Cosine have been applied to calculate the similarity of the query image and the gallery. The experimental results show that the proposed method achieves 0.85, 0.78 of accuracy by computing 7-top and 5-top accuracy, respectively.

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