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IEEE Conference on Wireless Communications and Networking

作者： Ibrahim M. Abou-Shehada Ali H. Muqaibel Wessam Mesbah Ki-Hong Park Mohamed-Slim Alouini Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical Engineering Department Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Indoor positioning systems (IPS) are gaining higher attention recently due to the increased demand for indoor location aware services. Visible light communication (VLC) is a promising technology to use for IPS. In particular, received signal strength (RSS) based visible light positioning (VLP) systems are gaining high attention due to their low complexity and cost, in addition to higher positioning accuracy compared to their radio frequency (RF) counterparts. One of the main challenges in RSS based VLP systems is encountered when the receiver (the target) is tilted and not placed in parallel with the transmitters (the anchors). RSS based trilateration techniques require a computationally expensive and time-consuming process to solve the nonlinear problem of tilted receivers. Fingerprint based systems generally provide high positioning accuracy with short positioning time, and maybe used to circumvent the need to deal with the high complexity associated with tilted receivers. However, the design of a fingerprinting VLP system for tilted receiver has not been explored yet as far as receivers with a single photodetector (PD) are concerned. In this work, a fingerprint based VLP system for tilted receivers using artificial neural networks (ANN) is proposed, where different types of input features for training the positioning algorithm are studied. We show that using the components of the normal vector to the PD's surface in addition to RSS values provides an excellent positioning accuracy with an average positioning error of 25.41 cm and a remarkably low average positioning time less than $\mathbf{5} \boldsymbol{\mu} \boldsymbol{s}$ . In addition, important research directions for future work are discussed.

关键词： Training Accuracy Receivers Artificial neural networks Fingerprint recognition Vectors Real-time systems

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Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization

arXiv

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arXiv 2024年

作者： Song, Sanghyeob Lew, Jaihyun Jang, Hyemi Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Samsung Electro-Mechanics Korea Republic of

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Most early methods, though, assume that the given image pairs are from the same camera or have minor lighting differences. Consequently, while these methods perform effectively under such conditions, they generally fail when input image pairs come from different domains, referred to as multimodal image pairs. To address these limitations, we propose AltO, an unsupervised learning framework for estimating homography in multimodal image pairs. Our method employs a two-phase alternating optimization framework, similar to Expectation-Maximization (EM), where one phase reduces the geometry gap and the other addresses the modality gap. To handle these gaps, we use Barlow Twins loss for the modality gap and propose an extended version, Geometry Barlow Twins, for the geometry gap. As a result, we demonstrate that our method, AltO, can be trained on multimodal datasets without any ground-truth data. It not only outperforms other unsupervised methods but is also compatible with various architectures of homography estimators. The source code can be found at: https://***/songsang7/AltO Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

arXiv

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arXiv 2022年

作者： Jang, Jaehee Ha, Heonseok Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Electrical and Computer Engineering Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks. © 2022, CC BY.

关键词： Deep neural networks

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Global distortions from local rewards: neural coding strategies in path-integrating neural systems 24

Global distortions from local rewards: neural coding strateg...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Francisco Acosta Fatih Dinc William T. Redman Manu Madhav David Klindt Nina Miolane Department of Physics UC Santa Barbara CNC Program Stanford University Applied Physics Laboratory Johns Hopkins University Biomedical Engineering University of British Columbia NeuroAI CSHL Electrical & Computer Engineering UC Santa Barbara

ISBN: (纸本)9798331314385

Grid cells in the mammalian brain are fundamental to spatial navigation, and therefore crucial to how animals perceive and interact with their environment. Traditionally, grid cells are thought support path integration through highly symmetric hexagonal lattice firing patterns. However, recent findings show that their firing patterns become distorted in the presence of significant spatial landmarks such as rewarded locations. This introduces a novel perspective of dynamic, subjective, and action-relevant interactions between spatial representations and environmental cues. Here, we propose a practical and theoretical framework to quantify and explain these interactions. To this end, we train path-integrating recurrent neural networks (piRNNs) on a spatial navigation task, whose goal is to predict the agent's position with a special focus on rewarded locations. Grid-like neurons naturally emerge from the training of piRNNs, which allows us to investigate how the two aspects of the task, space and reward, are integrated in their firing patterns. We find that geometry, but not topology, of the grid cell population code becomes distorted. Surprisingly, these distortions are global in the firing patterns of the grid cells despite local changes in the reward. Our results indicate that after training with location-specific reward information, the preserved representational topology supports successful path integration, whereas the emergent heterogeneity in individual responses due to global distortions may encode dynamically changing environmental cues. By bridging the gap between computational models and the biological reality of spatial navigation under reward information, we offer new insights into how neural systems prioritize environmental landmarks in their spatial navigation code.

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Terahertz Time-Domain Imaging for Radiotherapy Treatment Monitoring in Pancreatic Ductal Adenocarcinoma

Terahertz Time-Domain Imaging for Radiotherapy Treatment Mon...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Chakraborty, Debamitra Mills, Bradley N. Cheng, Jing Komissarov, Ivan Gerber, Scott Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623-1299 United States Department of Surgery University of Rochester Medical Center RochesterNY14642 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

ISBN: (纸本)9781957171395

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Tissue

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Multi-Resonant Class-E2 DC-DC Converter based on Voltage-Controlled Oscillator

Multi-Resonant Class-E2 DC-DC Converter based on Voltage-Con...

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IEEE Kansas Power and Energy Conference

作者： Lucas Sangoi Mendonça Valmir de Oliveira Academic Department of Electronics Federal University of Technology-Paraná Curitiba Brazil Academic Department of Electronics Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná Curitiba Brazil

ISBN: (数字)9798350372403

ISBN: (纸本)9798350372410

This paper shows the design and implementation of a multi-resonant Class-E 2 DC-DC converter based on voltage- controlled oscillator. By cascading a Class-E inverter to a Class-E rectifier, a double soft-switching converter is obtained, in which, the active switch turns-on with zero voltage switching and the diode turns-off with zero derivative voltage switching. This work relies on design equations based on normalized charts that relate the resonant frequency to the current transfer function, quality factor and peak values for the switch variables. In order to activate the switch, a circuit scheme is proposed based on a voltage-controlled oscillator connected to a low-side gate-driver. Therefore, it is not necessary to use a microcontroller neither auxiliary power sources. Experimental results are presented to show the feasibility of the proposed converter.

关键词： Q-factor Voltage-controlled oscillators Transfer functions Resonant frequency Rectifiers Switches DC-DC power converters

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A Transparent Ultrasound Array for Real-Time Optical,Ultrasound,and Photoacoustic Imaging

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Biomedical engineering Frontiers 2022年第1期3卷 284-297页

作者： Haoyang Chen Sumit Agrawal Mohamed Osman Josiah Minotto Shubham Mirg Jinyun Liu Ajay Dangi Quyen Tran Thomas Jackson Sri-Rajasekhar Kothapalli Department of Biomedical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA School of Electrical Engineering and Computer Science The Pennsylvania State UniversityUniversity ParkPA 16802USA Penn State Cancer Institute The Pennsylvania State UniversityHersheyPA 17033USA Graduate Program in Acoustics The Pennsylvania State UniversityUniversity ParkPA 16802USA

Objective and Impact *** imaging of ultrasound and optical contrasts can help map structural,functional,and molecular biomarkers inside living subjects with high spatial *** is a need to develop a platform to facilitate this multimodal imaging capability to improve diagnostic sensitivity and ***,combining ultrasound,photoacoustic,and optical imaging modalities is challenging because conventional ultrasound transducer arrays are optically *** a result,complex geometries are used to coalign both optical and ultrasound waves in the same field of *** elegant solution is to make the ultrasound transducer transparent to ***,we demonstrate a novel transparent ultrasound transducer(TUT)linear array fabricated using a transparent lithium niobate piezoelectric material for real-time multimodal *** TUT-array consists of 64 elements and centered at~6 MHz *** demonstrate a quad-mode ultrasound,Doppler ultrasound,photoacoustic,and fluorescence imaging in real-time using the TUT-array directly coupled to the tissue mimicking *** TUT-array successfully showed a multimodal imaging capability and has potential applications in diagnosing cancer,neurological,and vascular diseases,including image-guided endoscopy and wearable imaging.

关键词： Ultrasound specificity transparent

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Safety Helmet Detection Using Deep Learning: Implementation and Comparative Study Using YOLOv5, YOLOv6, and YOLOv7

Safety Helmet Detection Using Deep Learning: Implementation ...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Nigel Dale Then Yung W. K. Wong Filbert H. Juwono Zee Ang Sim Dept. of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Computer Science Program University of Southampton Malaysia Johor Malaysia

ISBN: (纸本)9781665486644

The safety of construction site personnel is highly dependent on the adherence of personal protective equipment (PPE) wearing. Safety helmet monitoring has become a popular topic in recent years as a result of the success in the field of image processing. Deep learning (DL) is widely used in object detection tasks due to its ability to create features based on raw data. Constant improvements in the DL models have led to numerous successful outcomes in the implementation of safety helmet detection tasks. The performance of different DL algorithms from previous studies will be assessed and studied in this review paper. The YOLOv5s (small) model, YOLOv6s (small) model, and the YOLOv7 model will be trained and evaluated in this paper.

关键词： Personal protective equipment Deep learning Head Computational modeling Object detection Feature extraction Safety

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Diabetic Retinopathy Detection: Improving Accuracy Using Multiple Transfer Learning Features from Pre-trained Deep Learning Networks

Diabetic Retinopathy Detection: Improving Accuracy Using Mul...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Kelvin Ka Yung Tiong W. K. Wong Filbert H. Juwono I. M. Chew Dept. of Electrical and Computer Engineering Curtin University Malaysis Miri Malaysia Computer Science Program University of Southampton Malaysia Nusajaya Malaysia

ISBN: (纸本)9781665486644

Diabetic Retinopathy (DR) is a type of complications caused by diabetes. Patients with DR may experience worsening vision, blindness, and eye pain. To effectively address this disorder, DR must be identified and classified according to its severity. Therefore, automated diagnosis of fundus lesions is of great interest for DR early detection. The development of deep learning technology has provided a strong foundation for effective implementation of the automated detection system. In particular, transfer learning techniques have greatly benefited the research community to reduce computation and reuse trained features. In this paper, the outputs from the ”average pooling” and ”fully connected” layers are used as the features to the Support Vector Machine (SVM) classifier with Error Correction Output Code (ECOC). The proposed method outperforms the fine-tuned pre-trained networks in predicting the severity classes with an accuracy of 80.1%. This means that multiple features extracted from the pre-trained networks contribute to a better recognition process.

关键词： Support vector machines Deep learning Training Retinopathy Pain Transfer learning Blindness

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Utilizing probabilistic entanglement between sensors in quantum networks

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Physical Review Applied 2024年第6期22卷 064085-064085页

作者： Emily A. Van Milligen Christos N. Gagatsos Eneet Kaur Don Towsley Saikat Guha Department of Physics The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Program in Applied Mathematics The University of Arizona Tucson Arizona 85721 USA Cisco Quantum Lab Los Angeles USA College of Computer Science University of Massachusetts Amherst Massachusetts USA Department of Electrical and Computer Engineering The University of Maryland College Park Maryland 20742 USA

One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete-variable case and quantum squeezing in the continuous-variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure that all sensors preshare entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.

关键词： Quantum entanglement

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