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Saliva-Sensing Dental Floss: A Novel Tool for Assessing Daily Stress via On-Demand Salivary Cortisol Measurement Integrating Molecularly Imprinted Polymer and Thread Microfluidics

arXiv

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arXiv 2024年

作者： Sharma, Atul Hossain, Nafize Ishtiaque Thomas, Ayanna Sonkusale, Sameer Nano Lab. Advanced Technology Laboratory Tufts University MedfordMA United States Department of Electrical and Computer Engineering Tufts University MedfordMA United States Department of Psychology Tufts University MedfordMA02155 United States

Elevated stress negatively impacts health, increasing the risk of cardiovascular diseases, weak immunity, anxiety, and cognitive decline. Current lab-based methods lack real-time stress measurement, while saliva diagnostics offers a convenient and non-invasive alternative. This work proposes repurposing dental floss as a thread-analytical saliva diagnostic device. This method uses flossing to collect and transport saliva to a flexible electrochemical sensor via capillary microfluidics, where cortisol, a stress biomarker, is measured. By integrating an electropolymerized molecularly imprinted polymer specific to cortisol (cort-eMIP) on a porous-laser-engraved-graphene (PLEG) working electrode, the saliva-floss platform shows a detection range of 0.10-10000 pg mL-1 (R2 = 0.9916) with a detection limit of 0.023 pg mL-1 for cortisol (in buffer medium). The saliva-sensing dental floss provides results within 5 minutes. The thread-based microfluidic design minimizes interference and ensures consistent repeatability when testing artificial and actual human saliva samples, yielding 98.64%–102.4% recoveries with a relative standard deviation of 5.01%, demonstrating high accuracy and precision. Validated using human saliva samples in a stress study, it showed a high correlation (r= 0.9910) with conventional ELISA assays. Combined with a wireless readout, this saliva floss offers a convenient way to monitor daily stress levels and can be extended to detect other critical salivary biomarkers. © 2024, CC BY-NC-ND.

关键词： Biomarkers

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Comparative Study of Alternative-Pole Flux Switching and Inter-Modular Biased-Flux Permanent Magnet Motors

Comparative Study of Alternative-Pole Flux Switching and Int...

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Electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Afrank Alireza Eikani Ehsan Farmahini Farahani Mojtaba Mirsalim Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K

This paper executes a comparative analysis between alternative-pole flux-switching and inter-modular biased-flux permanent magnet (PM) motors. Despite their close structural similarities, the former puts magnets not only between stator modules, but also close to the armature windings. However, the inter-modular PM (IMPM) motor only occupies spaces between stator modules. This type of occupation allows this structure to provide a safe operation point for PMs. Not considering the PM’s recoil-phenomenon, the torque density of the IMPM motor is not as significant as the alternative-pole flux-switching PM (AP-FSPM) motor. This is because of the four-operating mode of the AP-FSPM structure which enables it to increase the torque density by utilizing the PMs in every moment of the operation. But, when the demagnetization of the AP-FSPM’s PMs is taken into account, the torque density and torque per PM values of the AP-FSPM structure plummets. A complete full-load demagnetization analysis is done in order to get a better understanding of how the performance of the IMPM structure supersedes the AP-FSPM structure. 2D finite element analysis is used to extract the most important indexes of both topologies.

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Synthesized Data Generation for Enhanced Machine Learning in Dental X-ray Analysis: A Novel Approach to Age and Gender Prediction

Synthesized Data Generation for Enhanced Machine Learning in...

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International Conference on Biomedical engineering and Applications (ICBEA)

作者： M. I. R. Shuvo Awal Ahmed Fime Samuel Kim Hannah Lee Ian Jung Jong-Hoon Kim Young-Jin Jung Dong-Hun Han Department of Computer Science Advanced Telerobotics Research Lab Kent State University USA School of Healthcare and Biomedical Engineering Chonnam National University South Korea Department of Preventive and Social Dentistry School of Dentistry Seoul National University South Korea

ISBN: (数字)9798350375299

ISBN: (纸本)9798350375305

In the field of dental radiography, the precise estimation of age and gender based on X-ray pictures holds significant importance for a range of applications. Nevertheless, the scarcity of reliable and robust data presents a significant obstacle. This research highlights the significance of both data quality and data quantity in the context of deep learning models. The conventional methods of data augmentation are insufficient when used for dental X-ray pictures. To tackle this issue, we propose the introduction of an innovative data synthesis technique, which involves the extraction of dental structures from X-ray images and subsequently generates synthesized visual representations. Satisfactory results for age and gender prediction are achieved by employing the VGG19 and ResNet50 models, utilizing the synthesized data generated within the framework of the proposed research. The present study highlights the importance of comprehensive datasets and proposes a novel approach to improve the precision of dental radiography predictions.

关键词： Radiography Visualization Image processing Estimation Predictive models Data models Dentistry Reliability X-ray imaging Residual neural networks

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Rotation Exploration Transformer for Aerial Person Re-identification

Rotation Exploration Transformer for Aerial Person Re-identi...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Lei Wang Quan Zhang Junyang Qiu Jianhuang Lai School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pazhou Lab (HuangPu) Guangzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Aerial person re-identification (AReID) focuses on accurately matching target person images within a UAV camera network. Challenges arise due to the broad field of view and arbitrary movement of UAVs, leading to foreground target rotation and background style variation. Existing AReID methods have provided limited solutions for the former, while the latter remains largely unexplored. This paper propose a Rotation Exploration Vision Transformer (RoExViT) to tackle the aforementioned dual challenges. Specifically, we design Multiple Rotation Tokens (MRT) to explore diverse rotational representations at the feature level, addressing foreground target rotation. To handle background style variation, we propose Cross-Camera Similarity (CCS) loss to effectively minimize the view gap among different cameras. Furthermore, we propose Iteratively Adaptive Batch Construction (IABC) strategy to mitigate overfitting on small datasets. Extensive experiments show that our method outperforms the state-of-the-art methods on PRAI-1581 and UAV-Human while also exhibting outstanding performance on Market1501.

关键词： computer vision Transformers Cameras Autonomous aerial vehicles Identification of persons

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Investigating the Use of Ray Tracing for Signal-Level Radar Simulation in Space Surveillance

Investigating the Use of Ray Tracing for Signal-Level Radar ...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Mogamat Yaaseen Martin Simon Winberg Yunus Abdul Gaffar David Macleod Department of Electrical Engineering University of Cape Town Cape Town South Africa Advanced Computer Engineering Lab Centre for High Performance Computing Cape Town South Africa

The growing number of objects in the Low Earth Orbit is becoming increasingly concerning to astronomers and space missions - particularly due to the limitations in tracking accuracy due to the availability and placements of surveillance sensors. This paper connects to this issue, presenting the development of an open-source, ray-traced, signal-level pulsed radar simulator for use in exploring and planning space monitoring solutions without using real-time data feeds. The simulator was also developed around the use of the NVIDIA® OptiX™ engine to accommodate its ray-tracing features and accelerate performance. This was designed to aid astronomers and researchers in space situational awareness applications through the simulation of radar designs for orbital surveillance experiments. The developed tool was also compared against a more streamlined application that used point-model approximations for quick-look simulations, and the trade-offs between both simulators were evaluated. While the use of ray tracing resulted in significant speed costs, it was found that the algorithm also introduced more realistic results relative to point-model simulations - providing various advantages in scenarios involving shadowing and multiscatter.

关键词： Space vehicles Spaceborne radar Surveillance Space missions Space debris Ray tracing Radar tracking

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Inserting anybody in diffusion models via celeb basis 23

Inserting anybody in diffusion models via celeb basis

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Ge Yuan Xiaodong Cun Yong Zhang Maomao Li Chenyang Qi Xintao Wang Ying Shan Huicheng Zheng School of Computer Science and Engineering Sun Yat-sen University and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China and Guangdong Key Laboratory of Information Security Technology and Tencent AI Lab Tencent AI Lab The Hong Kong University of Science and Technology School of Computer Science and Engineering Sun Yat-sen University and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China and Guangdong Key Laboratory of Information Security Technology

Exquisite demand exists for customizing the pretrained large text-to-image model, e.g. Stable Diffusion, to generate innovative concepts, such as the users themselves. However, the newly-added concept from previous customization methods often shows weaker combination abilities than the original ones even given several images during training. We thus propose a new personalization method that allows for the seamless integration of a unique individual into the pre-trained diffusion model using just one facial photograph and only 1024 learnable parameters under 3 minutes. So we can effortlessly generate stunning images of this person in any pose or position, interacting with anyone and doing anything imaginable from text prompts. To achieve this, we first analyze and build a well-defined celeb basis from the embedding space of the pre-trained large text encoder. Then, given one facial photo as the target identity, we generate its own embedding by optimizing the weight of this basis and locking all other parameters. Empowered by the proposed celeb basis, the new identity in our customized model showcases a better concept combination ability than previous personalization methods. Besides, our model can also learn several new identities at once and interact with each other where the previous customization model fails to. Project page is at: http://***. Code is at: https://***/ygtxr1997/CelebBasis.

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Single Frame Lidar-Camera Calibration Using Registration of 3D Planes

Single Frame Lidar-Camera Calibration Using Registration of ...

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IEEE International Conference on Robotic Computing (IRC)

作者： Ashutosh Singandhupe Hung Manh La Quang Phuc Ha Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA School of Electrical and Data Engineering University of Technology Sydney Australia

ISBN: (纸本)9781665472616

This work focuses on finding the extrinsic parameters (rotation and translation) between Lidar and an RGB camera sensor. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The plane coefficients extracted from the sensor’s data are used to construct a well-structured set of 3D points. These 3D points are then ’aligned,’ which gives the relative transformation between the two sensors. We use our proposed Correntropy Similarity Matrix Iterative Closest Point (CoSMICP) Algorithm to estimate the relative transformation. This work uses a single frame of the point cloud data acquired from the Lidar sensor and a single frame from the calibrated camera data to perform this operation. From the camera image, we use the projection of the calibration target’s corner points to compute the 3D points, and along the process, we calculate the 3D plane equation using the corner points. We evaluate our approach on a simulated dataset with complex environment settings, making use of the freedom to assess under multiple configurations. Through the obtained results, we verify our method under various configurations.

关键词： Point cloud compression Three-dimensional displays Laser radar Transmission line matrix methods Robot vision systems Cameras Real-time systems

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Sentence-State LSTMs For Sequence-to-Sequence Learning 10th

Sentence-State LSTMs For Sequence-to-Sequence Learning

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Bai, Xuefeng Li, Yafu Zhang, Zhirui Xu, Mingzhou Chen, Boxing Luo, Weihua Wong, Derek Zhang, Yue School of Engineering Westlake University Hangzhou China Alibaba DAMO Academy Hangzhou China NLP2CT Lab Department of Computer and Information Science University of Macau China Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou China

ISBN: (纸本)9783030884796

Transformer is currently the dominant method for sequence to sequence problems. In contrast, RNNs have become less popular due to the lack of parallelization capabilities and the relatively lower performance. In this paper, we propose to use a parallelizable variant of bi-directional LSTMs (BiLSTMs), namely sentence-state LSTMs (S-LSTM), as an encoder for sequence-to-sequence tasks. The complexity of S-LSTM is only O(n) as compared to O(n2) of Transformer. On four neural machine translation benchmarks, we empirically find that S-SLTM can achieve significantly better performances than BiLSTM and convolutional neural networks (CNNs). When compared to Transformer, our model gives competitive performance while being 1.6 times faster during inference. © 2021, Springer Nature Switzerland AG.

关键词： Neural machine translation

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Empirical Study of Computational Intelligence Approaches for the Early Detection of Autism Spectrum Disorder 5th

Empirical Study of Computational Intelligence Approaches for...

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5th International Conference on Intelligent Computing and Applications, ICICA 2019

作者： Khatun, Mst. Arifa Ali, Md. Asraf Ahmed, Md. Razu Noori, Sheak Rashed Haider Sahayadhas, Arun Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Artificial Intelligence Research Lab Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (纸本)9789811555657

The objective of the research is to develop a predictive model that can significantly enhance the detection and monitoring performance of Autism Spectrum Disorder (ASD) using four supervised learning techniques. In this study, we applied four supervised-based classification techniques to the clinical ASD data obtained from 704 patients. Then, we compared the four machine learning (ML) algorithms performance across tenfold cross-validation, ROC curve, classification accuracy, F1 measure, precision, recall, and specificity. The analysis findings indicate that Support Vector Machine (SVM) achieved the uppermost performance than the other classifiers in terms of accuracy (85%), f1 measure (87%), precision (87%), and recall (88%). Our work presents a significant predictive model for ASD that can effectively help the ASD patients and medical practitioners. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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A Complex Hermitian Positive Definite Manifold Embedding Transformer Network for Time-Varying Direction of Arrival Tracking

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Teng, Liping Wang, Qing Wang, Yanhui Guo, Qinghua Li, Wanqing Tianjin University School of Electrical and Information Engineering Tianjin300072 China Nantong University School of Information Science and Technology Nantong226019 China Science and Technology on Space Physics Laboratory Beijing100076 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

In this work, we address the challenge of stable and robust time-varying direction-of-arrival (DOA) tracking, where both the DOA trajectory and the number of sources change over time across different scenarios. We formulate the problem as a multi-label classification task and propose a novel solution leveraging Riemannian manifold geometry and advanced neural networks for sequential data processing. Specifically, we introduce the Complex Hermitian Positive Definite embedding Transformer (C-HPD-T) network, which learns statistical representations of covariance matrices. The C-HPD-T consists of an HPD module and a Transformer module. The HPD module learns an appropriate statistical representation and the complex Transformer module models the data temporally. In particular, in the C-HPD-T network, we build a complex singular value decomposition (SVD) module to ensure that complex structure is preserved during the calculation of each layer. Meanwhile, we design a Transformer-based sequential network to capture the temporal relationship of signals and for the time-varying DOAs. Through training the network with varying numbers of moving targets featuring diverse motion trajectories, the network demonstrates excellent performance and robustness against array imperfections. Extensive simulation results demonstrate its superior performance gains compared to existing methods. © 2015 IEEE.

关键词： Covariance matrix

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