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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Theresia Herlina Rochadiani Yaya Heryadi Ilvico Sonata Lukas Wawan Suparta BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models—three, four, and five classes—with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively.

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BIOMIMETIC ANCHORING SYSTEM FOR SUSTAINED AND LOCALIZED GASTROINTESTINAL DRUG DELIVERY

BIOMIMETIC ANCHORING SYSTEM FOR SUSTAINED AND LOCALIZED GAST...

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2022 Solid-State Sensors, Actuators and Microsystems Workshop, Hilton Head 2022

作者： Levy, Joshua A. Straker, Michael A. Beardslee, Luke A. Ghodssi, Reza Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Institute for Systems Research University of Maryland College ParkMD United States Department of Materials Science and Engineering University of Maryland College ParkMD United States Fischell Department of Bioengineering University of Maryland College ParkMD United States Robert E. Fischell Institute for Biomedical Devices University of Maryland College ParkMD United States

ISBN: (纸本)9781940470047

We present a novel application of biomimetic spiny microneedles for an anchoring drug deposit (SMAD) enabling long-term localized treatment of lesions within the GI tract. Mechanical characterizations in ex-vivo intestinal tissue demonstrate a 22-fold higher anchoring force than traditional conical molded microneedle (MMN) arrays. Additionally, payload diffusion experiments demonstrate that the SMAD can predictably deliver drugs, locally, to a site of interest with comparable performance to MMNs, indicated by strong logarithmic correlation (R2=0.9773) across SMAD and MMN data. We anticipate this work will permit exploration of localized GI tract drug delivery, facilitating targeted treatment to reduce adverse drug side effects. © 2022 TRF.

关键词： Tissue

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Exploring Nanoscale Photoresponse Mechanisms for Enhanced Photothermoelectric Effects in van der Waals Interfaces

arXiv

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arXiv 2024年

作者： Xu, Da Liu, Qiushi Liang, Boqun Yu, Ning Ma, Xuezhi Xu, Yaodong Taniguchi, Takashi Lake, Roger K. Yan, Ruoxue Liu, Ming Department of Electrical and Computer Engineering University of California - Riverside RiversideCA92521 United States Department of Chemical and Environmental Engineering University of California - Riverside RiversideCA92521 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science Ibaraki Tsukuba305-0044 Japan Materials Science and Engineering Program University of California - Riverside RiversideCA92521 United States

Integrated photodetectors are crucial for their high speed, sensitivity, and efficient power consumption. In these devices, photocurrent generation is primarily attributed to the photovoltaic (PV) effect, driven by electron-hole separations, and the photothermoelectric (PTE) effect, which results from temperature gradients via the Seebeck effect. As devices shrink, the overlap of these mechanisms—both dependent on the Fermi level and band structure—complicates their separate evaluation at the nanoscale. This study introduces a novel 3D photocurrent nano-imaging technique specifically designed to distinctly map these mechanisms in a Schottky barrier photodiode featuring a molybdenum disulfide and gold (MoS2-Au) interface. We uncover a significant PTE-dominated region extending several hundred nanometers from the electrode edge, a characteristic facilitated by the weak electrostatic forces typical in 2D materials. Unexpectedly, we find that incorporating hexagonal boron nitride (h-BN), known for its high thermal conductivity, markedly enhances the PTE response. This counterintuitive enhancement stems from an optimal overlap between thermal and Seebeck profiles, presenting a new pathway to boost device performance. Our findings highlight the capability of this imaging technique to not only advance optoelectronic applications but also to deepen our understanding of light-matter interactions within low-dimensional systems. © 2024, CC BY.

关键词： Van der Waals forces

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Gene Name Recognition in Gene Pathway Figures Using Siamese Networks

Gene Name Recognition in Gene Pathway Figures Using Siamese ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Micheal Olaolu Arowolo Muhammad Azam Fei He Mihail Popescu Dong Xu Department of Public Health Sciences Health Informatics Program Xavier University of Louisiana New Orleans Louisiana USA Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia Missouri USA Department of Health Management and Informatics University of Missouri Columbia Missouri USA

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-consuming process, and current optical character recognition (OCR) methods are not effective in identifying gene names within these figures. To improve gene name recognition from pathway figures, we utilized a Siamese network that maps image segments to a library of pictures containing known gene name, like object recognition in many photo applications. We combined the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net) to create a CNN triple loss function. We trained and tested the developed TSPP-Net network using 563 gene images from 45 pathway pictures, 1000 images of alphanumeric characters and gene names from HUGO and KEGG. To benchmark against major available OCR technologies, we used 20 randomly selected curated pathway genes. Additionally, we compared several models, such as VGG19, VGG16, Resnet-50 Resnet-18, Densnet-121, and Xception for the Siamese backbone network model. VGG16 achieved the best performance with an accuracy of 93%, which is much higher than the results obtained using OCR. Our code is publicly available at https://***/MuhammadAzam636/Siamese-Network.

关键词： Image recognition Accuracy Sensitivity Text recognition Biological system modeling Optical character recognition Neural networks Libraries Object recognition Biological information theory

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Joint Learning of Nonlinear Representation and Projection for Fast Constrained MRSI Reconstruction

TechRxiv

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TechRxiv 2023年

作者： Li, Yahang Ruhm, Loreen Wang, Zepeng Zhao, Ruiyang Anderson, Aaron Arnold, Paul Huesmann, Graham Henning, Anke Lam, Fan Department of Bioengineering Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology Cancer Center at Illinois University of Illinois Urbana-Champaign UrbanaIL61801 United States The Department of Bioengineering The Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States The Department of Electrical and Computer Engineering The Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States The Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States Carle Foundation Hospital United States The Advanced Imaging Research Center University of Texas Southwestern Medical Center Max Planck Institute for Biological Cybernetics United States

Learning and utilizing low-dimensional representations for high-dimensional imaging problems is an active research area. We present here a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data, a unique high-dimensional molecular imaging modality. The proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data;(b) a joint formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint;and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem. The proposed method has been evaluated using simulations and different types of in vivo MRSI data, demonstrating robust improved performance over state-of-the-art methods. Computational complexity and algorithm convergence analysis have been performed to offer further insights into the effectiveness of the proposed method. © 2023, CC BY.

关键词： Molecular imaging

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High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models

arXiv

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arXiv 2023年

作者： Zhao, Ruiyang Peng, Xi Kelkar, Varun A. Anastasio, Mark A. Lam, Fan The Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign IL61801 United States The Department of Radiology Mayo Clinic RochesterMN55902 United States The Department of Bioengineering Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign IL61801 United States

We present a novel method that integrates subspace modeling with an adaptive generative image prior for high-dimensional MR image reconstruction. The subspace model imposes an explicit low-dimensional representation of the high-dimensional images, while the generative image prior serves as a spatial constraint on the "contrast-weighted" images or the spatial coefficients of the subspace model. A formulation was introduced to synergize these two components with complimentary regularization such as joint sparsity. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-model-based representation for images with varying contrasts, validated by experimental data. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged a recently developed intermediate layer optimization technique for network inversion. We evaluated the utility of the proposed method in two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MRSI. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases. Our work demonstrated the potential of integrating data-driven and adaptive generative models with low-dimensional representation for high-dimensional imaging problems. © 2023, CC BY.

关键词： Image reconstruction

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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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Conference on Lasers and Electro-Optics (CLEO)

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York Laboratory for Laser Energetics University of Rochester Rochester New York Department of Surgery University of Rochester Medical Center Rochester New York Department of Electrical and Computer Engineering University of Rochester Rochester New York

ISBN: (纸本)9798350369311

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.

关键词： Maximum a posteriori estimation Terahertz wave imaging Laser theory Sensitivity Refractive index Radiation therapy Time-domain analysis Spatial resolution Probes Monitoring

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Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation

arXiv

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arXiv 2022年

作者： Zhang, Tim Lammie, Corey Azghadi, Mostafa Rahimi Amirsoleimani, Amirali Ahmadi, Majid Genov, Roman Department of Bioengineering McGill University MontrealH3A 0G4 Canada College of Science and Engineering James Cook University QLD4814 Australia Department of Electrical Engineering and Compute Science York University TorontoONM3J 1P3 Canada Department of Electrical and Computer Engineering University of Windsor Windsor Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementing spike sorting algorithms is burgeoning. However, there is a lack of a systematic approach and a set of standardized evaluation criteria to facilitate direct comparison of both software and hardware implementations. In this paper, we formalize a set of standardized criteria and a publicly available synthetic dataset entitled Synthetic Simulations Of Extracellular Recordings (SSOER), which was constructed by aggregating existing synthetic datasets with varying Signal-To-Noise Ratios (SNRs). Furthermore, we present a benchmark for future comparison, and use our criteria to evaluate a simulated Resistive Random-Access Memory (RRAM) In-Memory Computing (IMC) system using the Discrete Wavelet Transform (DWT) for feature extraction. Our system consumes approximately (per channel) 10.72mW and occupies an area of 0.66mm2in a 22nm FDSOI Complementary Metal-Oxide-Semiconductor (CMOS) process. Copyright © 2022, The Authors. All rights reserved.

关键词： Metals

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Fast Tracking of Maximum Power in a Shaded Photovoltaic System Using Ali Baba and the Forty Thieves (AFT) Algorithm

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Processes 2023年第10期11卷

作者： Rehman, Khalil Ur Sajid, Injila Lu, Shiue-Der Ahmad, Shafiq Liu, Hwa-Dong Bakhsh, Farhad Ilahi Tariq, Mohd Sarwar, Adil Lin, Chang-Hua Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh202002 India Department of Electrical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan Department of Electrical Engineering National Institute of Technology Srinagar190006 India Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO. © 2023 by the authors.

关键词： Efficiency

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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