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EventLFM:event camera integrated Fourier light field microscopy for ultrafast 3D imaging

Light(Science & Applications) 2024年第7期13卷 1401-1415页

作者： Ruipeng Guo Qianwan Yang Andrew S.Chang Guorong Hu Joseph Greene Christopher V.Gabel Sixian You Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Physiology and Biophysics Boston UniversityBostonMA 02215USA. Neurophotonics Center Boston UniversityBostonMA 02215USA. Research Laboratory of Electronics(RLE)in the Department of Electrical Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA. Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Ultrafast 3D imaging is indispensable for visualizing complex and dynamic biological *** scanning-based techniques necessitate an inherent trade-off between acquisition speed and space-bandwidth product(SBP).Emerging single-shot 3D wide-field techniques offer a promising alternative but are bottlenecked by the synchronous readout constraints of conventional CMOS systems,thus restricting data throughput to maintain high SBP at limited frame *** address this,we introduce EventLFM,a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy(LFM),a state-of-theart single-shot 3D wide-field imaging *** event camera operates on a novel asynchronous readout architecture,thereby bypassing the frame rate limitations inherent to conventional CMOS *** further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by *** results demonstrate that EventLFM can robustly reconstruct fast-moving and rapidly blinking 3D fluorescent samples at kHz frame ***,we highlight EventLFM’s capability for imaging of blinking neuronal signals in scattering mouse brain tissues and 3D tracking of GFP-labeled neurons in freely moving *** believe that the combined ultrafast speed and large 3D SBP offered by EventLFM may open up new possibilities across many biomedical applications.

关键词： field linking overcome

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Optimal Placement of Solar Power Plant by Using AVOA Algorithm

Optimal Placement of Solar Power Plant by Using AVOA Algorit...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Klington, A. George Karthikeyan, K. St. Mother Theresa Engineering College Department of Computer Science Engineering Tuticorin India St. Mother Theresa Engineering College Department of Electrical and Electronics Engineering Tuticorin India

ISBN: (纸本)9798331521394

Infrastructure development and its design are more complex because of its constraint on budget and efficiency. By remembering these difficulties, a modified electrical structure is necessary for the nation. The power system structure should not depend on the conventional system. This main criterion is being in our engineer's mind. But, strengthening loads and their necessaries do not accept them to sleep. To face complex structures and constraints-based infrastructure facilities, optimal places for renewable systems are necessary. Here, the optimal placement of solar power plant is taken as the primary concern and its related analysis is made. Even though many algorithms are coming from all over the world. The part of those algorithms is not analyzed in many environments. In this paper, an AVOA algorithm is used for our analysis. This part of our research makes us excited with its results. The IEEE 57 system is used for the analysis. © 2025 IEEE.

关键词： African vultures optimization algorithm CPF MATPOWER PSO PV cell voltage stability

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Improving GNSS Reliability for Autonomous Vehicles by Detecting and Classifying Spoofing Attacks with Shallow Learning Models

Improving GNSS Reliability for Autonomous Vehicles by Detect...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ezzelarab, Mohamed A. Abdelhalim, Eman Mohamed, Mohamed A. Abosekeen, Ashraf Mansoura University Department of Electronics and Communication Engineering Mansoura Egypt Military Technical College Department of Electrical and Computer Engineering Cairo Egypt

ISBN: (纸本)9798350351408

Spoofing attacks on the Global Navigation Satellite System (GNSS) present significant threats to the security and reliability of autonomous vehicle systems, vital in civilian and military operations. Spoofing involves manipulating a vehicle's navigation system with false GNSS signals, potentially causing it to deviate from its path or crash. This study uses supervised Machine Learning (ML) models to detect GNSS spoofing attacks. These models were developed and tested using a dataset of authentic GNSS signals collected from diverse locations. Additionally, three types of GNSS spoofing attacks-simple, intermediate, and advanced-were simulated to mimic real-world scenarios using specialized equipment. The results of our experiments demonstrate the effectiveness of supervised ML in detecting and mitigating GNSS spoofing attacks. Testing accuracy rates of approximately 99.2% and 99.1% were achieved for the Decision Tree (DT) and K-Nearest Neighbor (KNN) algorithms, respectively. These findings underscore the importance of our approach in enhancing the accuracy and reliability of GNSS-based location services for autonomous vehicles. This research offers valuable insights for improving GNSS implementations and lays the groundwork for future advancements in spoofing detection methods. © 2024 IEEE.

关键词： Decision Tree (DT) Global Navigation Satellite System (GNSS) K-Nearest Neighbor (KNN) Machine Learning (ML) Spoofing and Unmanned Aerial Vehicles (UAVs)

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IoT-Based Intelligent Energy Management for EV Charging Stations

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第11期51卷 1853-1861页

作者： Dasi, Swathi Bondalapati, Swarupa Rani Subbaraju, Mummidi P. Nimma, Divya Jangir, Pradeep Reddy, R. Vijaya Kumar Zareena, N. Electronics and Communication Engineering Department Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Aditya University Andhra Pradesh Surampalem India University of Southern Mississippi United States Biosciences Department Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India associated with Applied Science Research Center Applied Science Private University Amman Jordan. Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Computer Science and Engineering Department R.V.R. & J. C. College of Engineering Guntur India

The electronics panels and controllers used in electric vehicle (EV) charging stations need to function securely and optimally in countries such as India, where the external temperature remains high for a significant part of the year. This study is the first to use a solar-battery storage integrated switchable glazing architecture to supply HVAC (heating, ventilation, and air conditioning) in the control rooms of electric vehicle charging stations throughout the day. The EV charging station has been equipped with a rooftop-mounted solar PV source as part of an initiative to promote renewable energy and sustainable forms of mobility. The utilization of a vanadium redox flow battery (VRFB) by the system ensures energy security through the provision of a durable solution for storing energy over an extended period of time. A smart scheduling system utilizing IoT technology was demonstrated to effectively meet the glazing load need of buildings in real-time, taking into account dynamic climatic conditions. This system incorporates solar PV, VRFB storage, and the neighborhood power grid. The efficiency of the suggested method was validated using four separate transitory scenarios: sunny, intermittently cloudy, prolongedly cloudy, and low solar radiation with frequent grid outages. Due to its versatility to be applied in various situations, the suggested method has the potential to greatly enhance capacity. © (2024), (International Association of Engineers). All rights reserved.

关键词： Air conditioning

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Classification of cervical spine disease using convolutional neural network

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Multimedia Tools and Applications 2024年第41期83卷 88963-88979页

作者： Bezabh, Yohannes Agegnehu Salau, Ayodeji Olalekan Abuhayi, Biniyam Mulugeta Ayalew, Aleka Melese Department of Information Technology University of Gondar Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Cervical spondylosis myelopathy (CSM) is a degenerative disorder of the cervical spine’s disks and joints, leading to neurological disability. Typically, this condition causes increasing discomfort and neurologic deterioration. Cervical spine pain disease classification using deep learning can assist medical professionals in making early and more precise medical diagnosis of cervical abnormalities. The main focus of the assessment for doctors is the classification of cervical spine diseases from X-rays images, which is valuable for patients to indicate the various symptoms they are faced with. Identifying cervical spine pain injuries (CPI) is a critical aspect of the diseases segmentation and classification. Manually explaining the high-dimensional feature space makes it challenging to identify the exact category and level of severity. This paper introduces a Convolutional Neural Networks (CNN) concatenation feature extraction method to help in the diagnosis of cervical spine pain disease from X-ray images. An innovative CNN-concatenation model-based fully connected classifier and a four-class system are applied to classify X-ray image data in this research. The CNN approach is employed to automatically classify X-ray images into healthy (normal) and with cervical spine disease (abnormal). CSM is currently diagnosed mostly through clinical evaluation using imaging methods such as X-ray and computed tomography (CT). Due to its inexpensive cost and minimal radiation dose, X-ray is commonly used. The proposed approach achieved a training accuracy of 99.98%, a validation accuracy of 98.29%, and a testing accuracy of 97.82% of classification from experiments. These results indicate that the image preprocessing, data augmentation, and CNN approaches provide an efficient classification method for identifying and accurately classifying cervical spine diseases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Optimal Placement of Solar Power Plant by Using MOTHFLAME Algorithm

Optimal Placement of Solar Power Plant by Using MOTHFLAME Al...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Karthikeyan, K. George Klington, A. St. Mother Theresa Engineering College Department of Electrical and Electronics Engineering Tuticorin India St. Mother Theresa Engineering College Department of Computer Science Engineering Tuticorin India

ISBN: (纸本)9798331521394

This study recommends the best location for a solar power plant in an IEEE 69-bus system to maintain voltage stability. PV recitation learning is done in an IEEE 69-bus test system using MATPOWER to develop voltage stability. A power device's balance is impacted when it experiences unexpected loading. To increase the stability from the diseases, compensation is required. In this case, the machine is examined utilizing CPF to improve stability. The overall performance of the suggested system is assessed using a variety of operating conditions, including those with and without PV, with a PV system utilising PSO, and with PV placement (using the Mothflame method). The findings indicate that a device with PV (adjusted with Mothflame's assistance) performs better than a device without PV and a device with a PV system installed by PSO. © 2025 IEEE.

关键词： Electric power system stability

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A Hyperbolic Micromagnet for Multiple Spin Qubits with Fast Rabi Oscillations and High Addressability

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第4期45卷 526-529页

作者： Chang, Che-Hao Li, Yu-Cheng Wu, Yu-Jui Liao, Chen-Yao Lin, Min-Jui Tsao, Hung-Yu Li, Jiun-Yun Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Graduate School of Advanced Technology Taipei10617 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan

We demonstrate a hyperbolic micromagnet to increase the manipulation speed of electron spins in silicon quantum dots using electric dipole spin resonance (EDSR). For single qubits, the hyperbolic magnet provides a large gradient of transverse magnetic fields of 4.9 mT/nm with the corresponding Rabi frequency ( 69 MHz). We then demonstrate that this hyperbolic micromagnet can drive a linear array of qubits with Rabi frequencies of 19 69 MHz and a high addressability (minimum difference of Larmor frequencies) up to 590 MHz to suppress the crosstalk between different qubits. We show that 15 qubits can be effectively addressed for high fidelity by this hyperbolic micromagnet. © 1980-2012 IEEE.

关键词： Magnetic fields

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A Robust Evolutionary Gravity Neocognitron Neural Network Model for Construction Cash Flow Prediction in Complex Project Management

A Robust Evolutionary Gravity Neocognitron Neural Network Mo...

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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Joseph, X.Feix Selvaraj, G. Babu, M. Arumuga Banu, A. Shajitha Bhuvanesh, A. Keerthanadevi, R. Loyola Institute of Technology and Science Department of Electrical and Electronics Engineering Kanyakumari India Ramco Institute of Technology Department of Mathematics Rajapalayam India Christ the King Engineering College Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

In order to keep expenses in check while building projects are being carried out, accurate cash flow forecasting is crucial. The project's unique features and risk considerations make it challenging, despite extensive study, to develop a reliable model for predicting future cash flows. Furthermore, both sequence and non-sequence elements have a significant influence on the building projects' cash flow. As a result, the authors of this research suggested a new inference model based on artificial intelligence (AI) called Evolutionary Gravity Neocognitron Neural Network (EG-NN). This model takes project complexity into account and attempts to handle non-sequence elements in time series issues. By attaining the highest values of 2.35%, MAPE of 5.65%, MAE of 2.03%, and coefficient of determination of 0.99, the experimental findings on thirteen construction projects have validated the EG-NN as the top model for cash flow forecasting. When compared to other AI-based inference models, the statistical result shows that cash flow forecasting can be improved by at least 14.1 percent in terms of root-mean-square error (RMSE) and 15.2 percent in terms of mean absolute error (MAE). Managers may benefit from the EG-NN network model's ability to predict and manage construction projects' cash flow. © 2024 IEEE.

关键词： Project management

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Development of IoT based Smart Campus using STM32 15

Development of IoT based Smart Campus using STM32

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raju, Leo Thirunavukkarasu, K. SSN College of Engineering Department of Electrical and Electronics Engineering Chennai India Thanthai Periyar Govt Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331300579

Over the past few decades, energy has been used mindlessly leading to many problems like reduction in supply, high power rates, increased carbon footprint, etc. Smart campuses, home automation, smart grid, etc. make living comfortable and economical. It optimizes energy consumption and is quite simple to operate too. This project proposes Internet of Things (IoT) based automation for smart car parking inside the campus, thus converting existing campus into smart campus. In this work, we have developed a prototype for smart campus using STM32 microcontroller. This microcontroller has better features compared to other contemporary microcontrollers. This prototype includes a smart car parking system which reduces traffic congestion and facilitates car parking inside the campus. © Grenze Scientific Society, 2024.

关键词： Traffic congestion

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The design and analysis of an advanced hybrid renewable energy microgrid for Cyangugu Remote rural community in Rwanda using Homer Pro

The design and analysis of an advanced hybrid renewable ener...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Asiegbu, Adimchinobi Almaktoof, Ali Adonis, Marco Electrical Electronics and Computer Engineering Department Cape Peninsula University of Technology Cape Town South Africa

ISBN: (纸本)9781032803722

In Rwanda, and in Cyangugu Remote community near the Kivu Lake, there is high solar sunshine energy, significant wind energy, abundant methane gas, appreciable geothermal energy, peat(biomass), hydro renewable energy sources, and grid power access. To ensure feasibility, accessibility, and sustainability, a balance need to be maintained to optimize this energy mix options, in terms of cost (NPV, COE etc.), maintenance, sensitivity, optimization, and other system parameters. The best combination of these Hybrid Renewable Energy Sources (HRES) considering the community’s energy needs, is modelled using the Hybrid Optimization of Multiple Electric Renewables – Professional (HOMER Pro), innovatively, to design and find out the proposed optimum system requirements with environmental advantages or benefits, best payback duration, lower or zero net GHGs and radiation emissions, performance optimization with lest value of Net Present Cost (NPC) and Cost of Energy (COE), sensitivity analysis involving components supply changes like wind speed change based atmospheric conditions, variable sunshine frequencies based on time of the day, seasons, fuel price changes etc. These seven energy supply components is used to provide access to modern, clean and reliable energy to Cyangugu. © 2025 The Author(s).

关键词： Clean energy

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