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IEEE Transactions on Radar Systems 2024年 2卷 866-879页

作者： DeLude, C. Driscoll, J. Mukherjee, M. Rahman, N. Mao, X. Kamal, U. Khan, S. Sivaraman, H. Huang, E. Mcharg, J. Swaminathan, M. Pande, S. Mukhopadhyay, S. Romberg, J. Georgia Institute of Technology School of Computer Science AtlantaGA30332 United States MIT Lincoln Laboratory Advanced Concepts and Technologies Group LexingtonMA02421 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Department of Electrical Engineering State CollegePA16802 United States

In this article, we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line (TDL) model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of M objects all interacting with one another, the TDL model's computational requirements scale as O(M3) per sample: there are O(M2) channels, each with O(M) complexity. In this article, we develop a new "direct path"model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an O(M2) per-sample scaling of the computational requirements. The impact of this is drastic, a 200-object scenario sees about a 100× reduction in the number of per-sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle-level C++ simulator of a hardware implementation of the model. © 2023 IEEE.

关键词： C++ (programming language)

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Measurement of peripheral nerve magnetostimulation thresholds of a head solenoid coil between 200 Hz and 88.1 kHz

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IEEE Journal of Translational engineering in Health and Medicine 2025年 13卷 275-285页

作者： Barksdale, Alex C. Ferris, Natalie G. Mattingly, Eli Sliwiak, Monika Guerin, Bastien Wald, Lawrence L. Davids, Mathias Klein, Valerie MIT Department of Electrical Engineering and Computer Science CambridgeMA United States Martinos Center for Biomedical Imaging CharlestownMA United States Harvard University Harvard Graduate Program in Biophysics CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology BostonMA United States Harvard Medical School BostonMA United States

Objective: Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC). Methods and procedures: PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds. Results: Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies. Conclusion: Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz. Clinical impact: These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation. © 2013 IEEE.

关键词： Neuroimaging

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Implementing Secure Key Management and Distribution in Cloud Environment 2

Implementing Secure Key Management and Distribution in Cloud...

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Lamsal, Aman Paul, Aniket Buchade, Amar Nalavade, Jagannath Bidwe, Ranjeet MIT Art Design and Technology University Dept. of Computer Science and Engineering Pune India Vishwakarma Institute of Information Technology Pune Savitribai Phule Pune University Pune India Department of Computer Engineering Pune India

ISBN: (纸本)9798350333763

Key management is considered as an essential aspect of securing cloud-based systems and data. As more organizations move their operations to cloud, the need for secure and efficient key management solutions becomes increasingly important. En-suring security of cloud computing is essential as many users often store their sensitive information with the cloud providers. Cloud service providers offer Key Management Services (KMS) to manage cryptographic keys for their customers, but there are still several challenges related to key management in cloud environments, including key storage, access control, and key rotation. Our research project aims to address these challenges by developing a secure and efficient key management solution for cloud-based systems. We propose a system that uses a combination of various cloud services and encryption algorithms to generate, store, and manage cryptographic keys securely. Our system provides granular access controls, and strong encryption to ensure the confidentiality and integrity of the keys. We believe that our proposed solution will help cloud service providers and organizations to manage their cryptographic keys more efficiently and securely, reducing the risk of unauthorized access and data breaches. This research paper provides a method to effectively manage, store and share the secret key between the parties using the concept of Threshold Cryptography. © 2023 IEEE.

关键词： Access control

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Evaluating Predictive Efficacy of Machine Learning-Based Morphological Analysis for Technological Forecasting 2nd

Evaluating Predictive Efficacy of Machine Learning-Based Mor...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Kumar, Piyush Bokhare, Anuja Rao, Madhuri Atur Centre Gokhale Cross Road Model Colony Maharashtra Pune411016 India Department of Computer Science Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Paud Road Kothrud Maharashtra Pune411018 India Department of Computer Science and Applications Dr. Vishwanath Karad MIT World Peace UniversityPaud Road Maharashtra Pune411018 India

ISBN: (纸本)9789819768097

The aim of the research presented in this paper is to assess the predictive capabilities of technology forecasting of morphological analysis employed with machine learning algorithms. Forecasting precision is desired to be as high as achievable;however, there are many limitations of morphological analysis in forecasting that includes both methodical approaches as well as technological constraints. Morphological analysis is characterized with domain specific presumptions and exclusive judgments that adds greater uncertainties and hinders advancements in technology. In this paper, a novel hybrid model is proposed with an aim to increase forecasting accuracy. Machine learning techniques such as Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression are integrated with morphological analysis in the proposed model. The output achieved could enable decision-makers with more reliable predictions. Trend research has been carried out very carefully in order to design an effective system. Various scenarios are considered in planning the approach of the proposed system where the Delphi method is also considered. The result of the simulation study shows clearly that the proposed model yields higher prediction accuracy which could provide valuable insights to decision-makers when navigating and exploring technology forecasting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Logistic regression

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A Study To Detect Emotions From Twitter Text Using Machine Learning Algorithms

A Study To Detect Emotions From Twitter Text Using Machine L...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Anusha Savitha A Shenoy S V Harish Department of Computer Science and Engineering MIT Manipal Udupi Department of Computer Science and Engineering SMVITM Bantakal Udupi

One of the main factor contributing to mental illness, which has been linked to an increased risk of dying young is depression. Additionally, it significantly contributes to suicide ideation. Although there are many underlying reasons of depression, social networking sites play a key part in raising the likelihood of depression. In recent years social media has become the integral part of our daily lives. User reflects his internal life in the content he shares in his social media platform like twitter. People share happy incidents, joyful memories and sad moments through tweets. Thus it is possible to forecast depression in people using Twitter data. Various machine learning techniques have been employed to analyze these data. The algorithms employed are Naïve Bayes and Logistic Regression. Those algorithms will produce intended outcomes.

关键词： Technological innovation Machine learning algorithms Social networking (online) Blogs Machine learning Depression Information exchange

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Evasion Techniques in Cybersecurity: An In-Depth Analysis

Evasion Techniques in Cybersecurity: An In-Depth Analysis

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Turukmane, Anil V Khekare, Ganesh Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science & Engineering Vellore Institute of Technology-AP Amaravati India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Because of the rising sophistication of cybersecurity threats, it is imperative to have a thorough grasp of the evasion strategies used by malevolent actors. This study offers a detailed analysis of evasion strategies in several cybersecurity sectors, such as intrusion detection systems, network security, and endpoint protection. This paper employs a methodical approach to examine the development of these methods, showcases actual case studies, and suggests approaches for detection and mitigation. The findings emphasize how crucial it is to conduct ongoing research and development to combat new cyber threats. Key discoveries are reviewed, and directions for further research are indicated. © 2024 IEEE.

关键词： Network intrusion

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Predictive Chemistry Augmented with Text Retrieval

arXiv

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arXiv 2023年

作者： Qian, Yujie Li, Zhening Tu, Zhengkai Coley, Connor W. Barzilay, Regina Computer Science and Artificial Intelligence Lab MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Chemical Engineering MIT United States

This paper focuses on using natural language descriptions to enhance predictive models in the chemistry field. Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from the literature. In this paper, we introduce TextReact, a novel method that directly augments predictive chemistry with texts retrieved from the literature. TextReact retrieves text descriptions relevant for a given chemical reaction, and then aligns them with the molecular representation of the reaction. This alignment is enhanced via an auxiliary masked LM objective incorporated in the predictor training. We empirically validate the framework on two chemistry tasks: reaction condition recommendation and one-step retrosynthesis. By leveraging text retrieval, TextReact significantly outperforms state-of-the-art chemoinformatics models trained solely on molecular data. © 2023, CC BY.

关键词： Information retrieval

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Grading of Fruit Ripeness Using Arduino in IoT

Grading of Fruit Ripeness Using Arduino in IoT

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Saini, Dilip Kumar Jang Bahadur Ahammad, Sk Hasane Das, Purushottam Bhatt, Ashutosh Malusare, Pragati Pande, Sandeep Dwarkanath School of Engineering and Technology Pimpri Chinchwad University Department of Computer Science and Engineering Pune India Koneru Lakshmaiah Education Foundation Department of E.C.E. Vaddeswaram522302 India Graphic Era Hill University Cse Dehradun India Graphic Era Deemed to Be University Cse Dehradun India Himalayan School of Science & Technology Swami Rama Himalayan University Uttarakhand Department of Computer Science and Engineering India Indira College of Engineering & Management Department of Computer Science and Engineering Pune India Academy of Engineering Mit Pune India

ISBN: (纸本)9798350329186

Ripening is the process by which the growing fruit will get the required quality and palatable nature. Grading fruits play an essential role in the food processing industry because by knowing the quality of the fruit, the vendor or the farmer can decide when he must sell his product to his customer. If the farmer or the fruit vendor knows the correct quality of the product, then he can sell his product at a better price, and he may get some profit. In this work, we supervise the quality of climacteric fruits like bananas and mango. It has been proposed to permit customers to purchase healthy fruits without actively contacting products and to benefit a predetermined set of individuals. The sensing element is the one that helps to detect the quality of the fruit, and here the sensing element is a gas sensor that is connected to the Arduino Uno. The information about the fruit will be sent to the user Via NodeMCU using Arduino Uno. © 2023 IEEE.

关键词： Fruits

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique

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Procedia computer science 2025年 258卷 4105-4116页

作者： Nilesh Bhaskarrao Bahadure Sudhanshu Gonge Jagdish Chandra Patni Bhoomi Shah Nagrajan Raju Prasenjeet D. Patil Department of Computer Science and Engineering School of Technology GSFC University Vadodara Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Thanjavur Tamil Nadu India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality.

关键词： Berkeley wavelet transformation enhancement magnetic resonance imaging (MRI) segmentation morphology

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Consistent diffusion meets tweedie: training exact ambient diffusion models with noisy data 24

Consistent diffusion meets tweedie: training exact ambient d...

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Proceedings of the 41st International Conference on Machine Learning

作者： Giannis Daras Alexandros G. Dimakis Constantinos Daskalakis Department of Computer Science University of Texas at Austin and Archimedes AI Department of Electrical and Computer Engineering University of Texas at Austin Department of Electrical Engineering and Computer Science MIT and Archimedes AI

Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data, solving an open problem in Ambient diffusion. Our key technical contribution is a method that uses a double application of Tweedie's formula and a consistency loss function that allows us to extend sampling at noise levels below the observed data noise. We also provide further evidence that diffusion models memorize from their training sets by identifying extremely corrupted images that are almost perfectly reconstructed, raising copyright and privacy concerns. Our method for training using corrupted samples can be used to mitigate this problem. We demonstrate this by fine-tuning Stable Diffusion XL to generate samples from a distribution using only noisy samples. Our framework reduces the amount of memorization of the fine-tuning dataset, while maintaining competitive performance.

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