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Laser 3D Manufacturing XII 2025

作者： Babayigit, Ceren Tavares-Negrete, Jorge Alfonso Esfandyarpour, Rahim Boyraz, Ozdal Department of Electrical Engineering and Computer Science The University of California IrvineCA92697 United States Department of Biomedical Engineering The University of California IrvineCA92697 United States Department of Mechanical and Aerospace Engineering The University of California IrvineCA92697 United States

ISBN: (纸本)9781510684560

Achieving high-resolution outputs in Digital Light Processing (DLP) bioprinting requires precise control over photocrosslinking, particularly for dense, intricate models with fine gaps. Over-crosslinking is affected by factors such as light scattering, limited depth of focus, and molecular diffusion. Traditionally, these issues are managed by altering the material and light source. This study presents a novel segmentation approach designed to address these challenges by leveraging the inherent diffusion characteristics of the DLP system rather than altering its core components. The generation and diffusion of free radicals increase with light intensity and the density of the illuminated area, which alters the risk of over-crosslinking in these regions. The proposed technique involves segmenting the photomask into discrete regions and employing an on-off activation strategy to enhance control over photopolymerization. By strategically segmenting the mask and alternating the activation of adjacent segments, the technique effectively manages the distribution and concentration of free radicals and allows time for the termination phase of photopolymerization. It creates spatial gaps, helps manage free radical diffusion, prevents unwanted combinations, and filling of small gaps, thus improving resolution. Demonstrated by printing a bronchial model, this approach achieved a 100 µm gap between bronchial branches at a 14.43x compression ratio to anatomical size. The live-dead assay shows sustained cell viability of up to 90% for GelMA and up to 85% for GelMA-PEGDA constructs across seven days. This study shows that the segmentation strategy significantly reduces over-crosslinking, advancing DLP bioprinting for applications requiring precise control over complex features and detailed features. © 2025 SPIE.

关键词： Photopolymerization

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Noise Robustness of Data Re-Uploading Quantum Classifiers 4

Noise Robustness of Data Re-Uploading Quantum Classifiers

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Molina, Daniel E. Hamilton, Kathleen University of Southern California Ming Hsieh Electrical and Computer Engineering Department Los AngelesCA United States Computational Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350343236

We investigate the robustness of quantum neural networks (QNNs) trained as binary classifiers against bit flip and depolarizing noise, two channels that emulate a subset of noise present in noisy intermediate-scale quantum (NISQ) devices. The presence of decoherence and other forms of noise poses significant challenges to classification accuracy. To assess the impact of noise on label extraction, and to evaluate the performance and robustness of our models, we trained three different label assignment methods in the presence of various levels of noise. Each QNN model uses 2 qubits to implement a parameterized model and either the qubits of the QNN, or a set of ancilla qubits are used for label extraction. These three different methods project the state of either a single qubit or multiple qubits onto the computational basis. This generates a probability distribution that is post-processed to assign a class label and a class probability. Each QNN is trained using noisy numerical simulation on a dataset with 2 features. Depolarizing noise channels are interleaved with the parameterized gates of the QNN and we include bit flip error prior to extracting the labels. The noise levels for the depolarizing noise and bit flip error are swept over large ranges of values. We deploy QNNs trained at various noise levels on IBM quantum computers- this introduces an additional source of noise due to SWAP gate noise. We observe that the QNNs trained with simulated noise can have good performance on NISQ hardware. © 2023 IEEE.

关键词： Depolarization

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DisCovHAR: Contrastive Attention for Human Activity Recognition Under Distribution Shifts

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IEEE Internet of Things Journal 2025年

作者： Chen, Luke Odema, Mohanad Al Faruque, Mohammad Abdullah University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Advances in Internet-of-Things (IoT) wearable sensors and Edge-Artificial Intelligence (Edge-AI) have enabled practical realizations of Machine Learning (ML)-enabled mobile sensing applications like Human Activity Recognition (HAR). The effective deployment of these data-driven models necessitates learning robust representations capable of handling prevalent Distribution Shifts (DS), including new users, device positions, rotations, and more. In that respect, Contrastive Learning (CL) has shown promise in learning transformation-invariant features, outperforming traditional HAR methods. However, recent findings reveal that the contrastive loss induces shrinkage and expansion of the feature space which may limit the generalization capacity of the model. To address this, we propose DisCovHAR, a contrastive attention method to selectively apply the contrastive loss to a subset of the feature space through the transformer encoder attention mechanism. Extensive experiments on three HAR datasets (DSADS, PAMAP2, USCHAD) demonstrate its superiority over state-of-the-art methods. Specifically, our approach yields up to 4.47% and 7.82% average accuracy improvements in subject-wise and position-wise generalization settings. Furthermore, DisCov-HAR demonstrates up to 5.07% increased robustness compared to prior methods under multivariate distribution shift scenarios. © 2014 IEEE.

关键词： Contrastive Learning

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Empirical Study on Mispronunciation Detection for Tajweed Rules during Quran Recitation 6

Empirical Study on Mispronunciation Detection for Tajweed Ru...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Alsahafí, Yousef S. Asad, Muhammad Department of Information technology Computing and Information technology Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350373875

This paper presents an extensive empirical study aiming to identify the optimal combination of feature extraction techniques and machine learning algorithms, including deep learning, for automated mispronunciation detection during Quran recitation. Three feature extraction methods-Mel-Frequency Cepstral Coefficients (MFCC), Spectrogram, and Kaldi pitch - are systematically evaluated in conjunction with a diverse set of classifiers, including Gradient Boosting (GB), Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The study leverages the QDAT dataset, comprising 1500 audio samples from 150 readers, with metadata specifying age, gender, and three distinct rules for mispronunciation detection. Experimental results demonstrate that the integration of MFCC with deep learning classifiers consistently achieves the highest performance across multiple evaluation metrics. Model robustness is validated using receiver operator characteristic curves (ROC) and accuracy metrics. © 2024 IEEE.

关键词： Speech recognition

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Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm

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Journal on Artificial Intelligence 2023年第1期5卷 15-30页

作者： Abdulghani M.Abdulghani Mokhles M.Abdulghani Wilbur L.Walters Khalid H.Abed Department of Electrical&Computer Engineering and Computer Science Jackson State UniversityJackson39217USA

The object detection technique depends on various methods for duplicating the dataset without adding more *** augmentation is a popularmethod that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit *** is recommended in the casewhere the amount of high-quality data is limited,and gaining new examples is costly and *** this paper,we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes(Car,Bus,Motorcycle,and Person).We used five different data augmentations techniques for duplicates and improvement of our *** performance of the object detection algorithm was compared when using the proposed augmented dataset with a combination of two and three types of data augmentation with the result of the original *** evaluation result for the augmented data gives a promising result for every object,and every kind of data augmentation gives a different *** mAP@.5 of all classes was 76%,and F1-score was 74%.The proposed method increased the mAP@.5 value by+13%and F1-score by+10%for all objects.

关键词： Artificial intelligence object detection YOLOv7 data augmentation data brightness data darkness data blur data noise convolutional neural network

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Developed an IoT-based smart solar energy monitoring system for environmental sustainability

Developed an IoT-based smart solar energy monitoring system ...

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International Conference on Advancement in electrical and Electronic engineering (ICAEEE)

作者： Abdullah Al Mamun Mehedi Hasan Shuvo Towhidul Islam Dwin Islam Md. Jakirul Islam Faysal Amin Tanvir Department Of Computer Science and Engineering DUET Gazipur Bangladesh Department of Electrical and Computer Engineering Lamar University Texas USA

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Currently, electricity demand is constantly increasing all over the world, and the demand for this electricity is much higher than the production. As a result, the whole world is facing a global problem. In this decade, many researchers have been concerned about it and have suggested many ways to generate electricity in several ways, but they are not all concerned about low-cost, user-friendly, reliable data loggers and monitoring systems. To resolve the issue, we proposed an intelligent and environment-friendly way to generate electricity in all areas of a developing country. Initially, heat is generated either through household cooking activities or by burning excess or unnecessary garbage. After that, the solar panels are charged from the generated heat, and the charge is stored in a battery from the solar panel. The data logger, which is based on the ESP32 microcontroller, records all monitoring parameters on the cloud and presents them in the Android application. All the processes are completed automatically without any human interaction, and burning unnecessary garbage establishes environmental sustainability. The data logger hardware prototype that has been created incorporates four sensors to measure temperature, humidity, voltage, and current, which show up on an Android application with real-time data on many metrics. Additionally, it provides maintenance professionals with crucial information about any problems related to battery charging. To complete the work, we have created the hardware implementation of photovoltaic systems that use the IoT for remote data collection to provide us sustainability, and this prototype’s total cost is affordable.

关键词： Temperature sensors Temperature measurement Voltage measurement Electricity Scalability Green products Solar energy

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Smart Greenhouse Control System for Sustainable Agriculture 1

Smart Greenhouse Control System for Sustainable Agriculture

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1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023

作者： Almarzouqi, Hamda A. Alfalasi, Fatma A. Almurbati, Noora T. Siraj Eddin, Mohammed Shikhli, Amir Abdul-Qawy, Antar S. H. Jarndal, Anwar University of Sharjah Electrical Engineering Department Sharjah United Arab Emirates Sumait University Department of Mathematics & Computer Science Zanzibar Tanzania United Republic of

ISBN: (纸本)9798350330137

The Industrial Revolution 4.0, which prioritizes internet-based device connectivity and big data, was brought about by the rapid advancement of science and technology. One of the key factors in the growth of this revolution is the Internet of Things (IoT). IoT is tremendously beneficial in many aspects of life, including smart greenhouses, which provide environmental conditions suited for plant growth and development. Greenhouse technology can benefit from IoT applications in agriculture, and a wide variety of greenhouses require continuous maintenance, monitoring, and care. It is essential to have a system that enables the owner to monitor and operate the greenhouse remotely because greenhouses are often located outside of cities, distant from their owners. This research project aims to develop a low-cost smart control system for greenhouses that will reduce costs, time, and effort, enable farmers to monitor the plants' health remotely, and automatically control greenhouse parameters like temperature, humidity, light, and water. The proposed system would allow remote plant monitoring, encouraging plant development and notifying users of unanticipated events that may harm the crops. The system will also assist in identifying small, constant changes that indicate an infection, ultimately preventing disease outbreaks. A smartphone application has been used to monitor the greenhouse continuously. With an excellent outcome, the system has been successfully designed, implemented, and tested. © 2023 IEEE.

关键词： Greenhouses

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The role of compute nodes in privacy-aware decentralized AI 6

The role of compute nodes in privacy-aware decentralized AI

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6th International Workshop on Embedded and Mobile Deep Learning, EMDL 2022 - Part of MobiSys 2022

作者： Tirana, Joana Pappas, Christodoulos Chatzopoulos, Dimitris Lalis, Spyros Vavalis, Manolis School of Computer Science University College Dublin VistaMilk Sfi Ireland Department of Electrical and Computer Engineering University of Thessaly Greece

ISBN: (纸本)9781450394048

Mobile devices generate and store voluminous data valuable for training machine learning (ML) models. Decentralized ML model training approaches eliminate the need for sharing such privacy-sensitive data with centralized entities by expecting each data owner that participates in an ML model training process to compute updates locally and share them with other entities. However, the size of state-of-the-art ML models and the computational needs for producing local updates in mobile devices prohibit the participation of mobile devices in decentralized training of such models. Split learning techniques can be combined with decentralized model training protocols to realize the involvement of mobile devices in model training while preserving the privacy of their data. Mobile devices can produce local updates by splitting the model they are training into multiple parts and delegating the processing of the computationally demanding parts to compute nodes. This work examines the impact of the number of available compute nodes and their interaction. We split ResNet101 ML model into 3,4, and 5 parts, keep the first and the last part in the data owner and assign the processing of the middle parts to compute nodes. Additionally, we analyze the training time when the compute nodes assist multiple data owners in parallel or are responsible for different model parts by forming a pipeline. © 2022 Owner/Author.

关键词： Sensitive data

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A Graph Data Model-based Micro-Provenance Approach for Multi-level Provenance Exploration in End-to-End Climate Workflows

A Graph Data Model-based Micro-Provenance Approach for Multi...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Fiore, Sandro Rampazzo, Mattia Elia, Donatello Sacco, Ludovica Antonio, Fabrizio Nassisi, Paola University of Trento Department of Information Engineering and Computer Science Trento Italy Cmcc Foundation Advanced Scientific Computing Division Lecce Italy

ISBN: (纸本)9798350324457

Open science is a vital part in the current and future research agenda worldwide. In order to meet Open science goals, it is of paramount importance to fully support the research process, which includes also properly addressing provenance and reproducibility of scientific experiments. Indeed, provenance and reproducibility are two key requirements for analytics workflows in Open science contexts. Handling provenance at different levels of granularity and during the entire experiment lifecycle becomes key to properly and flexibly managing lineage information related to large-scale experiments as well as enabling reproducibility scenarios. To this end, this work introduces the micro-provenance concept, and it provides an in-depth description of its design, implementation and exploitation in the context of a multi-model climate analytics workflow. © 2023 IEEE.

关键词： climate domain graph data model micro-provenance Open science reproducibility

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