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Laser science, LS 2013

作者： Hu, S. Kang, C. Zhao, Y. Mares, J.W. Vlasov, Y.A. Assefa, S. Weiss, S.M. Department of Physics and Astronomy Vanderbilt University Nashville TN 37235 United States Interdisciplinary Graduate Program in Materials Science Vanderbilt University Nashville TN 37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN 37235 United States IBM T.J. Watson Research Center Yorktown Heights NY 10536 United States

Multi-hole defect (MHD) photonic crystal cavities functionalized with in situ synthesized DNA bioreceptors are demonstrated for biosensing applications. By significantly increasing light interaction with target biomol... 详细信息

ISBN: (纸本)9781557529879

关键词： Photonic crystals

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Analysis of Gait Data Based on Deep Learning to Predict Multiple Balance Scale Scores

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IEEE Sensors Journal 2024年第1期24卷 920-930页

作者： Lin, Chueh-Ho Peng, Chih-Wei Lee, I-Jung Wu, Chi-Ming Lin, Bor-Shing Taipei Medical University International Ph.D. Program in Gerontology and Long-Term Care Taipei City110 Taiwan Taipei Medical University Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei City110 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei City110 Taiwan Taipei Medical University School of Gerontology and Long-Term Care College of Nursing Taipei City110 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237 Taiwan

Unbalanced walking is increasingly common among older adults;therefore, routinely assessing the balance of older adults is crucial. The traditional method of assessing balance uses scales, requires the supervision of a physical therapist (PT), and is time-consuming. The present study, therefore, proposes a deep learning (DL) model architecture that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network model to predict the scores of three scales, namely the Berg balance scale (BBS), timed up and go (TUG), and single-leg stance (SLS). The gait data of 15 m of walking were collected from seven inertial measurement units (IMUs) and input into the CNN-LSTM model for evaluation. The BBS and TUG only require the participants to wear two IMUs on the left and right thighs, respectively, for accurate predictions. The mean absolute errors (MAEs) of predicting the scores of the BBS and TUG are 1.2562 and 1.4016 s, respectively;however, the MAE of score predictions of SLS is higher than that of the BBS and TUG, indicating that gait data cannot be used for assessing SLS;moreover, participants only wear one IMU on the right calf for BBS and TUG evaluations, which yield MAEs of 1.4334 and 1.5229 s, respectively. The proposed system can quickly and accurately predict the scores of the BBS and TUG. The proposed model can assist PTs with making clinical decisions. © 2001-2012 IEEE.

关键词： Long short-term memory

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Photonic crystal defects with increased surface area for improved refractive index sensing

Photonic crystal defects with increased surface area for imp...

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Conference on Lasers and Electro-Optics, CLEO 2010

作者： Kang, Christopher Christopher Phare Weiss, Sharon M. Vlasov, Yurii A. Assefa, Solomon Interdisciplinary Graduate Program in Materials Science Vanderbilt University Nashville TN 37235 United States Department of Physics and Astronomy Vanderbilt University Nashville TN 37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University Tennessee 37235 United States IBM T.J. Watson Research Center Yorktown Heights NY 10536 United States

Photonic crystal cavities with tunable surface area via multiple-hole defects were investigated for increased resonance wavelength shifts upon exposure to variable-index analytes. Sensitivity was improved by 10% compa... 详细信息

ISBN: (纸本)9781557528896

关键词： Refractive index

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Photonic crystal defects with increased surface area for improved refractive index sensing

Photonic crystal defects with increased surface area for imp...

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Conference on Lasers and Electro-Optics

作者： Kang, Christopher Phare, Christopher Weiss, Sharon M. Vlasov, Yurii A. Assefa, Solomon Interdisciplinary Graduate Program in Materials Science Vanderbilt University Nashville TN 37235 United States Department of Physics and Astronomy Vanderbilt University Nashville TN 37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University 37235 United States IBM. T.J. Watson Research Center Yorktown Heights NY 10536 United States

Photonic crystal cavities with tunable surface area via multiple-hole defects were investigated for increased resonance wavelength shins upon exposure to variable-index analytes. Sensitivity was improved by 10% compar... 详细信息

ISBN: (纸本)9781557528902

关键词： Surface defects

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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international Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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Glucose Sensing Utilizing Complex-Valued Neural Networks 23

Glucose Sensing Utilizing Complex-Valued Neural Networks

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4th international Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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IoT-Enabled Deep Learning Algorithm for Estimation of State-of-Charge of Lithium-ion Batteries∗

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Journal of Circuits, Systems and computers 2024年第7期33卷 2450134-2450134页

作者： Pushpavanam, B. Kalyani, S. Prasanna, M. Arul Sangaiah, Arun Kumar Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Kamaraj College of Engineering and Technology Madurai India Department of Electrical and Electronics Engineering Kamaraj College of Engineering and Technology Madurai India International Graduate Institute of AI National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging-discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis. © 2024 World Scientific. All rights reserved.

关键词： Battery management systems

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Complex-valued functional link network design by orthogonal least squares method for function approximation problems

Complex-valued functional link network design by orthogonal ...

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international Joint Conference on Neural Networks (IJCNN)

作者： Md. Faijul Amin Ramasamy Savitha Muhammad Ilias Amin Kazuyuki Murase Department of Systems Design Engineering Graduate School of Engineering University of Fukui Fukui Japan School of Electrical and Electronics Engineering Nanyang Technological University Singapore Department of Computer Science and Engineering United International University Dhaka Bangladesh Research and Education Program for Life Science University of Fukui Fukui Japan

This paper presents a fully complex-valued functional link network (CFLN). The CFLN is a single-layered neural network, which introduces nonlinearity in the input layer using nonlinear functions of the original input variables. In this study, we consider multivariate polynomials as the nonlinear functions. Unlike multilayer neural networks, the CFLN is free from local minima problem, and it offers very fast learning in parameters because of its linear structure. In the complex domain, polynomial based CFLN has an additional advantage of not requiring activation functions, which is a major concern in the complex-valued neural networks. However, it is important to select a smaller subset of polynomial terms (monomials) for faster and better performance, since the number of all possible monomials may be quite large. In this paper, we use the orthogonal least squares method in a constructive fashion (starting from lower degree to higher) for the selection of a parsimonious subset of monomials. Simulation results demonstrate that computing CFLN in purely complex domain is advantageous than in double-dimensional real domain, in terms of number of connection parameters, faster design, and possibly generalization performance. Moreover, our proposed CFLN compares favorably with several other multilayer networks in the complex domain.

关键词： Polynomials Nonhomogeneous media Educational institutions Algorithm design and analysis Function approximation Neural networks Input variables

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Smart Environmental Architecture for Node Localization in a Wireless Sensor Network

Smart Environmental Architecture for Node Localization in a ...

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international Conference on Intelligent Environments (IE)

作者： Salvador Jauregui-Ortiz Mario Siller Felix Ramos Edson Scalabrin Electrical Engineering and Computer Science Department Center of Research and Advanced Studies of the IPN Guadalajara Jalisco Mexico Graduate Program in Computer Science Pontifical Catholic University Parana Brazil Curitiba Parana Brazil

As far as we know, there is not a Node Localization Algorithm (NLA) that presents the same accuracy for all possible scenarios. We believe that a NLA should be able to "interpret" the dynamic information of the environment. In this sense, simple NLAs are rather focused and might perform well for specific scenarios and applications. Therefore, information fusion and context awareness seems to be an appropriate approach to address this issue. We propose the Smart Environmental Architecture for Node Localization (SEA-NL), which is composed by two main elements: (i) the Smart Beacon Nodes (SBNs) and (ii) the Logical Position of Nodes (LPN). In (i) the obstruction level indicator is estimated and can improve the estimation of distances among nodes. In (ii) environment information and a one to one relation between a node and an object are used and can also improve location estimation. Via simulation, our architecture was tested indoors and outdoors considering three localization algorithms: the Weighed Centroid Localization (WCL), the Centroid Localization, and the Triangular Centroid Localization. Finally, we present an accuracy comparison among NLAs used in isolated way, and by using the SBNs, the LPN, and the SEA-NL, where our architecture improves WCL up to ~30.88%.

关键词： Wireless sensor networks Accuracy Estimation computer architecture Buildings Attenuation Monitoring

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ICE: An Intelligent Cognition Engine with 3D NAND-Based In-Memory Computing for Vector Similarity Search Acceleration 22

ICE: An Intelligent Cognition Engine with 3D NAND-Based In-M...

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Proceedings of the 55th Annual IEEE/ACM international Symposium on Microarchitecture

作者： Han-Wen Hu Wei-Chen Wang Yuan-Hao Chang Yung-Chun Lee Bo-Rong Lin Huai-Mu Wang Yen-Po Lin Yu-Ming Huang Chong-Ying Lee Tzu-Hsiang Su Chih-Chang Hsieh Chia-Ming Hu Yi-Ting Lai Chung-Kuang Chen Han-Sung Chen Hsiang-Pang Li Tei-Wei Kuo Meng-Fan Chang Keh-Chung Wang Chun-Hsiung Hung Chih-Yuan Lu Macronix International Co. Ltd. and Department of Electrical Engineering National Tsing Hua University Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology and Macronix International Co. Ltd. Institute of Information Science Academia Sinica Macronix International Co. Ltd Macronix International Co. Ltd. and Graduate Institute of Electronics Engineering National Taiwan University Department of Computer Science and Information Engineering National Taiwan University and Graduate Institute of Electronics Engineering National Taiwan University Department of Electrical Engineering National Tsing Hua University

ISBN: (纸本)9781665462723

Vector similarity search (VSS) for unstructured vectors generated via machine learning methods is a promising solution for many applications, such as face search. With increasing awareness and concern about data security requirements, there is a compelling need to store data and process VSS applications locally on edge devices rather than send data to servers for computation. However, the explosive amount of data movement from NAND storage to DRAM across memory hierarchy and data processing of the entire dataset consume enormous energy and require long latency for VSS applications. Specifically, edge devices with insufficient DRAM capacity will trigger data swap and deteriorate the execution performance. To overcome this crucial hurdle, we propose an intelligent cognition engine (ICE) with cognitive 3D NAND, featuring non-volatile in-memory computing (nvIMC) to accelerate the processing, suppress the data movement, and reduce data swap between the processor and storage. This cognitive 3D NAND features digital nvIMC techniques (i.e., ADC/DAC-free approach), high-density 3D NAND, and compatibility with standard 3D NAND products with minor modifications. To facilitate parallel INT8/INT4 vector-vector multiplication (VVM) and mitigate the reliability issue of 3D NAND, we develop a bit-error-tolerance data encoding and a two's complement-based digital accumulator. VVM can support similarity computations (e.g., cosine similarity and Euclidean distance), which are required to search "the most similar data" right where they are stored. In addition, the proposed solution can be realized on edge storage products, e.g., embedded MultiMedia Card (eMMC). The measured and simulated results on real 3D NAND chips show that ICE enhances the system execution time by 17× to 95× and energy efficiency by 11× to 140×, compared to traditional von Neumann approaches using state-of-the-art edge systems with MobileFaceNet on CASIA-WebFace dataset. To the best of our knowledge, this work demo

关键词： 3D NAND in-memory computing vector similarity search unstructured data search

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