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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Priyanka, K. Sridhara, N. Prasanna Kumar, M.J. Vijay Kumar, M.S. Nitte Meenakshi Institute of Technology Information Science & Engineering Department Bangalore India Adichunchanagiri University Bgs Institute of Technology Computer Science & Engineering Department BG Nagara India Mit Department of Ise Mysore India

ISBN: (纸本)9798350306927

This paper presents an innovative approach to poultry farming, leveraging cutting-edge technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and edge computing. Unlike conventional manual monitoring systems, the proposed system deploys an array of smart sensors for continuous real-time monitoring of critical environmental parameters, including temperature, humidity, gas levels, and lighting. The data collected by these sensors are processed through edge computing, enabling autonomous control of farm actuators to optimize conditions for poultry health and production. A significant emphasis is placed on ensuring food and water hygiene within the poultry environment. To mitigate the risk of contamination and potential disease spread, the system incorporates precise food measurement mechanisms tailored to the specific age of the chicks. This meticulous approach guarantees the dispensing of the appropriate amount of fresh and uncontaminated food during each feeding cycle, thereby maintaining a high standard of hygiene at the feeder. Ultimately, this proactive system significantly contributes to safeguarding poultry health and enhancing overall farm productivity while prioritizing disease control. By implementing these health-conscious practices, the proposed system ensures the production of robust and healthy chickens, meeting the stringent standards demanded by consumers and reshaping poultry farming practices for greater sustainability and efficiency. © 2023 IEEE.

关键词： Internet of things

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Multi-variable control to mitigate loads in CRISPRa networks

Multi-variable control to mitigate loads in CRISPRa networks

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IEEE Conference on Decision and Control

作者： Krishna Manoj Theodore W. Grunberg Domitilla Del Vecchio Department of Mechanical Engineering MIT MA USA Department of Electrical Engineering and Computer Science MIT MA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications.

关键词： Couplings Multiplexing Hands Degradation Sensitivity RNA Genetics Adaptive control

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Developing a Series of AI Challenges for the United States department of the Air Force∗

Developing a Series of AI Challenges for the United States D...

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2022 IEEE High Performance Extreme Computing Conference, HPEC 2022

作者： Gadepally, Vijay Angelides, Gregory Barbu, Andrei Bowne, Andrew Brattain, Laura J. Broderick, Tamara Cabrera, Armando Carl, Glenn Carter, Ronisha Cha, Miriam Cowen, Emilie Cummings, Jesse Freeman, Bill Glass, James Goldberg, Sam Hamilton, Mark Heldt, Thomas Huang, Kuan Wei Isola, Phillip Katz, Boris Koerner, Jamie Lin, Yen-Chen Mayo, David Mcalpin, Kyle Perron, Taylor Piou, Jean Rao, Hrishikesh M. Reynolds, Hayley Samuel, Kaira Samsi, Siddharth Schmidt, Morgan Shing, Leslie Simek, Olga Swenson, Brandon Sze, Vivienne Taylor, Jonathan Tylkin, Paul Veillette, Mark Weiss, Matthew L Wollaber, Allan Yuditskaya, Sophia Kepner, Jeremy MIT Lincoln Laboratory United States MIT Computer Science and Artificial Intelligence Laboratory United States United States Department of the Air Force United States MIT Electrical Engineering and Computer Science United States Atmospheric and Plenetary Science MIT Department of Earth United States MIT Institute for Medical Engineering & Science United States MIT Connection Science United States

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

American leadership in AI. These broad strategy documents have influenced organizations such as the United States department of the Air Force (DAF). The DAF-mit AI Accelerator is an initiative between the DAF and mit to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-mit AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances. © 2022 IEEE.

关键词： Large dataset

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What would Gauss say about representations? probing pretrained image models using synthetic Gaussian benchmarks 24

What would Gauss say about representations? probing pretrain...

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Proceedings of the 41st International Conference on Machine Learning

作者： Ching-Yun Ko Pin-Yu Chen Payel Das Jeet Mohapatra Luca Daniel Department of Electrical Engineering and Computer Science MIT Cambridge and IBM Research Yorktown IBM Research Yorktown Department of Electrical Engineering and Computer Science MIT Cambridge

Recent years have witnessed a paradigm shift in deep learning from task-centric model design to task-agnostic representation learning and task-specific fine-tuning. Pretrained model representations are commonly evaluated extensively across various real-world tasks and used as a foundation for different downstream tasks. This paper proposes a solution for assessing the quality of representations in a task-agnostic way. To circumvent the need for real-world data in evaluation, we explore the use of synthetic binary classification tasks with Gaussian mixtures to probe pretrained models and compare the robustness-accuracy performance on pretrained representations with an idealized reference. Our approach offers a holistic evaluation, revealing intrinsic model capabilities and reducing the dependency on real-life data for model evaluation. Evaluated with various pretrained image models, the experimental results confirm that our task-agnostic evaluation correlates with actual linear probing performance on downstream tasks and can also guide parameter choice in robust linear probing to achieve a better robustness-accuracy trade-off.

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DNN-Based Knee OA Severity Prediction System: Pathologically Robust Feature engineering Approach

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SN computer science 2023年第1期4卷 1-11页

作者： Ruikar, Darshan Kamble, Parshuram Ruikar, Amruta Houde, Kavita Hegadi, Ravindra School of Computer Science MIT World Peace University Kotharud Maharashtra Pune 411038 India School of Computer Science Central University of Karnataka Aland Road Karnataka Kadaganchi 585367 India Department of Computer Science and Engineering SPM Polytechnic Kumathe Maharashtra Solapur 413224 India

Osteoarthritis (OA) is a common chronic health condition of bone joints. It gradually leads severe pain and disability. Over the last 2 decades, the effect of OA is noticed more in the adult population, as well. People suffering from knee OA struggle a lot to perform daytoday activities, such as walking, climbing, and sitting, etc. The best available treatment to date tries to reduce pain but does not help to cure the chronic condition. The only possible solution is early detection and treatment of OA. There is great scope to develop an automated computer-aided diagnosis (CAD) system which will assist experts to predict the exact knee OA severity grade by analyzing X-ray images. The prediction accuracy of the CAD system is majorly dependent upon feature engineering. This means that the extracted features must truly represent the pathological features that are observed and analyzed by the experts during the diagnosis and prognosis. Hence, it is required that DNN models learn the features accurately. However, due to data scarcity and model design issues, DNN models are not able to extract better representative features. In this research attempt, we propose to combine engineered features (having high similarity with pathological features) into the features learned by the DNN model. We then perform classification on the combined feature space. The proposed system uses NIH-OAI radiographic dataset for the experimentation. The severity prediction follows K &L grades and yields 92.7% accuracy. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CAD Classification Deep neural network Eigenvalue Envelope K(Formula presented.) L grading system Knee X-ray images NIH-OAI Osteoarthritis Pathological features

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Negative Differential Resistance Regime of Field Emitter Arrays

Negative Differential Resistance Regime of Field Emitter Arr...

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Device Research Conference

作者： Y. Shin W. Chern N. Karaulac A. Akinwande Department of Electrical Engineering and Computer Science MIT MA 02139

ISBN: (数字)9798350373738

ISBN: (纸本)9798350373745

Silicon field emitter arrays (FEAs) -based cold cathodes have shown promise in many applications under harsh environments such as x-ray sources and high-power microwave devices due to the temperature independence of tunneling current and the property of ballistic transport [ 1 – 3 ] . We report an unexpected, yet reproducible Negative Differential Resistance (NDR) region within the device output characteristics of gated FEAs. These results were obtained by utilizing an on-chip flat silicon anode with etched stand-offs to define the anode-to-emitter distances of <100μm. Our analysis using calculations and simulation reveal that the parallel-plate configuration introduces a deceleration of electrons in the channel between the anode and gate when the anode voltage, V AE , is lower than the gate voltage, V GE .

关键词： Resistance Field emitter arrays Temperature Voltage Microwave devices Logic gates Tunneling

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Lateral Control of Hybrid Drones: Near-Optimal Power Disturbance Aware Policy

Lateral Control of Hybrid Drones: Near-Optimal Power Disturb...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Miroslav Kosanić Marija Ilić Department of Electrical Engineering and Computer Science MIT MA USA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In unmanned aerial vehicle (UAV) systems, achieving extended flight autonomy remains a significant challenge, even in hybrid systems utilizing both fuel and battery as energy sources. To extend the flight time, our paper introduces a novel application of online composite control that achieves fuel savings through disturbance awareness. Our contributions include the derivation of a nonlinear model of the energy-conversion dynamics and its connection with lateral dynamics. We discuss how these nonlinearities are linearized through timescale separation based on the operational rates of drone energy sources. The effectiveness of our composite control method is validated through real-world drone flight data. Numerical results show a reduction in fuel usage of approximately 4.5% through a disturbance-aware policy. This paper not only advances the fundamental understanding of composite control for bilinear time-scale separated systems but also opens new directions for research in trajectory optimization for hybrid powertrain systems.

关键词： Mechanical power transmission Predictive models Autonomous aerial vehicles Reliability engineering Batteries Numerical models Fuels Vehicle dynamics Trajectory optimization Drones

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Image Schema Decompositions of the Conceptual Dependency INGEST Primitive: A Study of Paraphrases 7

Image Schema Decompositions of the Conceptual Dependency ING...

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7th Image Schema Day, ISD7 2023

作者： Macbeth, Jamie C. Kilayko, Alexis Zhao, Zoie Song, Sophie Zheng, Winnie X. Department of Computer Science Smith College 10 Elm St NorthamptonMA01063 United States Department of Electrical Engineering and Computer Science MIT 77 Massachusetts Ave CambridgeMA02139 United States

One of the hallmarks of the Schank-Minsky Conceptual Dependency Trans-Frames meaning representation system is that it attempts to express complex meanings by building large and complex conceptual structures using a relatively small number of primitives. Recently comparisons of image schemas with Conceptual Dependency primitives revealed ways of possibly reducing the number of primitives while maintaining the expressiveness of the set—an important research goal because it increases the flexibility and richness of the primitive-decomposed structures in a way that better approximates human cognition. Inspired by this prior work, we employ a paraphrase generation system to explore the replacement of the Conceptual Dependency INGEST primitive by PTRANS and CONTAIN, which are the analogs of Source_Path_Goal and Containment image schemas. The results of the study bring us a step closer to a possible unification of image schemas with Conceptual Dependency. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Natural language processing systems

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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine Learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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Controlling quantum fluctuations of macroscopic light with sharply nonlinear gain

Controlling quantum fluctuations of macroscopic light with s...

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CLEO: science and Innovations, S and I 2022

作者： Nguyen, Linh Sloan, Jamison Rivera, Nicholas Soljačić, Marin Department of Physics MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States

ISBN: (纸本)9781557528209

Here, we introduce a mechanism of "sharp nonlinear gain" that strongly suppresses quantum fluctuations of lasers. This effect can lead to orders-of-magnitude suppression of intensity fluctuations in real lasers, as well as macroscopic sub-Poissonian light. © Optica Publishing Group 2022, © 2022 The Author(s)

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