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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Antonio Carlos M. de Queiroz Igor Oliveira Gameleiro COPPE/EP-Electrical Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil EP-Department of Electronic and Computer Engineering Federal University of Rio de Janeiro Rio de Janeiro Brazil

This work shows how balanced transconductor-C low-pass ladder filters using a different arrangement of the transconductors to reduce distortion can also have stable common-mode natural frequencies, not requiring common-mode stabilization circuits. Simulated distortion results are presented, interesting sensitivity properties of these filters are discussed, and some limitations of the technique are mentioned.

关键词： Passive filters Low pass filters Sensitivity Passband Prototypes Poles and zeros

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Appointments for 2005-2006 term

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2005年第7期13卷 773-782页

作者： Ranganathan, Nagarajan Agrawal, Vishwani D. Chakradhar, Srimat T. Chakrabarty, Krishnendu Courtois, Bernard DeMara, Ronald Hu, Xiaobo Sharon Ismail, Yehea I. Jha, Niraj K. John, Lizy Kurian Ker, Ming-Dou Koren, Israel Liu, Bin-Da Marculescu, Diana Marculescu, Radu Narayanan, Vijaykrishnan Nassif, Sani R. Nowick, Steven M. Sapatnekar, Sachin S. Sherlekar, Sunil Sylvester, Dennis Vemuri, Ranga Pham, Michael Department of Computer Science and Engineering University of South Florida Tampa FL 33620 United States Department of Computer Science and Engineering Nanomaterials and Nanomanufacturing Research Center University of South Florida Tampa United States IEEE United States Systems United States Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ United States IETE India ACM NEC Laboratories America Inc. Princeton NJ United States Duke University Durham NC United States ACM SIGDA Sigma Xi United States Laboratory of Techniques of Informatics and Microelectronics for Computer Architectecture ASME IMAPS IEEE Computer Society University of Central Florida Orlando United States Computer Engineering Program SEECS Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Northwestern University Evanston IL United States Department of Electrical Engineering Princeton University Princeton NJ United States Association for Computing Machinery Austin United States UT Austin Engineering Foundation United States Department of Electronics Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering University of Massachusetts Amherst United States Department of Electrical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Electrical and Computer Engineering Faculty Carnegie Mellon University Pittsburgh PA United States Computer Science and Engineering Department Pennsylvania State University University Park United States IBM Austin Research Laboratory Austin TX United States Columbia University New York NY United States Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States Tata Consultancy Services Centre of Excellence for Embedded Systems India Department of Electrical Engineering IIT Bombay India Department of Electrical Engineering Uni

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Reporting of Hospital Facility on Smartphone

Reporting of Hospital Facility on Smartphone

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2019 International Conference of Science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Rusdi, Jack Febrian Salam, Sazilah Abu, Nur Azman Sunaryo, Budi Agustina, Nova Gusdevi, Harya Hermansah Hadhiwibowo, Ari Heryati, Tuty Hardi, Richki Taufiq, Rohmat Jufri, Muhammad Taher Sudarsono, Ignatius Utomo, Eko Wahyu Kodong, Frans Richard Fannya, Puteri Informatics Engineering Sekolah Tinggi Teknologi Bandung Bandung Indonesia Information and Communication Technology Faculty Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Electrical Engineering Andalas University Padang Indonesia Rumah Sakit Umum Daerah Majalaya Kabupaten Bandung Majalaya Indonesia Informatics Engineering Computer Science Faculty Universitas Mulya Balikpapan Indonesia Department of Informatics Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang15117 Indonesia Program Studi Sistem Informasi Fakultas Teknik Dan Sistem Informasi Universitas Yapis Papua Indonesia Civil Engineering Department Engineering Faculty Universitas Langlangbuana Bandung Bandung Indonesia Department of Informatics Engineering UPN Veteran Yogyakarta Yogyakarta Indonesia Department of Medical Record and Health Information Faculty of Health Universitas Esa Unggul Jakarta Indonesia

In the context of achieving Good Corporate Governance (GCG) in hospitals, among others, it is carried out with control and supervision, including in the case of hospital facility maintenance installations. Besides that, the hospital needs to improve its services. The asset reporting system is one of the high needs in this field, so it requires a solution that can deal with existing problems. Unfortunately, the current reporting system so far is more likely to be conventional, such as by telephone. This study proposes a new solution to the hospital facility maintenance reporting process, namely by utilising smartphone-based applications that directly connected to the server. So, the information distributed to the competent parties. This solution can speed up the reporting process and actions. Also, save the historical equipment that is available from each service room - the results of this study used as a hospital facility maintenance reporting system. © Published under licence by IOP Publishing Ltd.

关键词： Hospitals

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Device Interoperability and Authentication for Telemedical Appliance based on the ISO/IEEE 11073 Personal Health Device (PHD) Standards

Device Interoperability and Authentication for Telemedical A...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Luther Paul R. Caranguian Susan Pancho-Festin Luis G. Sison a graduate student in the MS Electrical Engineering (Instrumentation and Control) program of University of the Philippines Diliman. Festin is with the Department of Computer Science University of the Philippines Diliman. with the Electrical and Electronics Engineering Institute University of the Philippines Diliman.

ISBN: (纸本)9781424441198

In this study, we focused on the interoperability and authentication of medical devices in the context of telemedical systems. A recent standard called the ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards addresses the device interoperability problem by defining common protocols for agent (medical device) and manager (appliance) interface. The X73-PHD standard however has not addressed security and authentication of medical devices which is important in establishing integrity of a telemedical system. We have designed and implemented a security policy within the X73-PHD standards. The policy will enable device authentication using Asymmetric-Key Cryptography and the RSA algorithm as the digital signature scheme. We used two approaches for performing the digital signatures: direct software implementation and use of embedded security modules (ESM). The two approaches were evaluated and compared in terms of execution time and memory requirement. For the standard 2048-bit RSA, ESM calculates digital signatures only 12% of the total time for the direct implementation. Moreover, analysis shows that ESM offers more security advantage such as secure storage of keys compared to using direct implementation. Interoperability with other systems was verified by testing the system with LNI Healthlink, a manager software that implements the X73-PHD standard. Lastly, security analysis was done and the system's response to common attacks on authentication systems was analyzed and several measures were implemented to protect the system against them.

关键词： Interoperability Telemedical Authentication

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ProPILE: probing privacy leakage in large language models 23

ProPILE: probing privacy leakage in large language models

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

作者： Siwon Kim Sangdoo Yun Hwaran Lee Martin Gubri Sungroh Yoon Seong Joon Oh Department of Electrical and Computer Engineering Seoul National University NAVER AI Lab University of Luxembourg and Parameter Lab Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University Parameter Lab and Tübingen AI Center University of Tübingen

The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. This paper presents ProPILE, a novel probing tool designed to empower data subjects, or the owners of the PII, with awareness of potential PII leakage in LLM-based services. ProPILE lets data subjects formulate prompts based on their own PII to evaluate the level of privacy intrusion in LLMs. We demonstrate its application on the OPT-1.3B model trained on the publicly available Pile dataset. We show how hypothetical data subjects may assess the likelihood of their PII being included in the Pile dataset being revealed. ProPILE can also be leveraged by LLM service providers to effectively evaluate their own levels of PII leakage with more powerful prompts specifically tuned for their in-house models. This tool represents a pioneering step towards empowering the data subjects for their awareness and control over their own data on the web. The demo can be found here: https://***/research/propile

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A novel approach for ellipsoidal outer-approximation of the intersection region of ellipses in the plane

arXiv

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arXiv 2017年

作者： Yousefi, Siamak Chang, Xiao-Wen Wymeersch, Henk Champagne, Benoit Toussaint, Godfried Department of Electrical and Computer Engineering McGill University MontrealQC Canada School of Computer Science McGill University MontrealQC Canada Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden Computer Science Program New York University Abu Dhabi United Arab Emirates

In this paper, a novel technique for tight outer-approximation of the intersection region of a finite number of ellipses in 2-dimensional (2D) space is proposed. First, the vertices of a tight polygon that contains the convex intersection of the ellipses are found in an efficient manner. To do so, the intersection points of the ellipses that fall on the boundary of the intersection region are determined, and a set of points is generated on the elliptic arcs connecting every two neighbouring intersection points. By finding the tangent lines to the ellipses at the extended set of points, a set of half-planes is obtained, whose intersection forms a polygon. To find the polygon more efficiently, the points are given an order and the intersection of the half-planes corresponding to every two neighbouring points is calculated. If the polygon is convex and bounded, these calculated points together with the initially obtained intersection points will form its vertices. If the polygon is non-convex or unbounded, we can detect this situation and then generate additional discrete points only on the elliptical arc segment causing the issue, and restart the algorithm to obtain a bounded and convex polygon. Finally, the smallest area ellipse that contains the vertices of the polygon is obtained by solving a convex optimization problem. Through numerical experiments, it is illustrated that the proposed technique returns a tighter outer-approximation of the intersection of multiple ellipses, compared to conventional techniques, with only slightly higher computational cost. Copyright © 2017, The Authors. All rights reserved.

关键词： Convex optimization

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High-Resolution Imaging of Human Cancer Proteins Using Microprocessor Materials

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ChemBioChem 2022年第17期23卷 e202200310-e202200310页

作者： Solares, Maria J. Jonaid, G.M. Luqiu, William Y. Berry, Samantha Khadela, Janki Liang, Yanping Evans, Madison C. Pridham, Kevin J. Dearnaley, William J. Sheng, Zhi Kelly, Deborah F. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Department of Biomedical Engineering Pennsylvania State University University Park 16802 PA United States Center for Structural Oncology Pennsylvania State University University Park 16802 PA United States Department of Electrical and Computer Engineering Duke University Durham 27708 NC United States Fralin Biomedical Research Institute at VTC Virginia Tech Roanoke 24016 VA United States

Mutations in tumor suppressor genes, such as Tumor Protein 53 (TP53), are heavily implicated in aggressive cancers giving rise to gain- and loss-of-function phenotypes. While individual domains of the p53 protein have been studied extensively, structural information for full-length p53 remains incomplete. Functionalized microprocessor chips (microchips) with properties amenable to electron microscopy permitted us to visualize complete p53 assemblies for the first time. The new structures revealed p53 in an inactive dimeric state independent of DNA binding. Residues located at the protein-protein interface corresponded with modification sites in cancer-related hot spots. Changes in these regions may amplify the toxic effects of clinical mutations. Taken together, these results contribute advances in technology and imaging approaches to decode native protein models in different states of activation. © 2022 Wiley-VCH GmbH.

关键词： cancer electron microscopy microchips p53, molecular modeling

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An Explainable Deep Learning Model for Prediction of Severity of Alzheimer’s Disease

An Explainable Deep Learning Model for Prediction of Severit...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Godwin Ekuma Daniel B. Hier Tayo Obafemi-Ajayi Computer Science Dept. Missouri State University Springfield MO USA Electrical & Computer Eng. Dept Missouri University of Science & Technology Rolla MO USA Engineering Program Missouri State University Springfield MO USA

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer’s disease (AD). AD diagnosis and prognosis heavily rely on neuroimaging information, particularly magnetic resonance imaging (MRI). We present a deep learning model framework that integrates a local data-driven interpretation method that explains the relationship between the predicted AD severity from the CNN and the input MR brain image. The deep explainer uses SHapley Additive exPlanation values to quantity the contribution of different brain regions utilized by the CNN to predict outcomes. We conduct a comparative analysis of three high-performing CNN models: DenseNet121, DenseNet169, and Inception-ResNet-v2. The framework shows high sensitivity and specificity in the test sample of subjects with varying levels of AD severity. We also correlated five key AD neurocognitive assessment outcome measures and the APOE genotype biomarker with model misclassifications to facilitate a better understanding of model performance.

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An Open-Source GPU-Based Acoustic Simulator for Fast and Accurate Modeling of Acoustic Scattering

An Open-Source GPU-Based Acoustic Simulator for Fast and Acc...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Zixuan Tian Yun Jing Aiguo Han Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana USA Graduate Program in Acoustics Pennsylvania State University USA Department of Biomedical Engineering and Mechanics Virginia Polytechnic Institute and State University Blacksburg USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Fast, accurate, and realistic simulations of ultrasonic scattering in biological tissues play an important role in biomedical ultrasound research. This study introduces UltraWave, a new open-source ultrasound simulation tool we developed for accurately modeling acoustic and elastic wave propagation in two- and three-dimensional heterogeneous media. UltraWave allows the utilization of multiple graphics processing units (GPUs) to deliver faster and more accurate full-wave simulations. The perfectly matched layer was integrated into the simulator. UltraWave was validated against well-established scattering theories in a variety of basic scattering scenarios, and achieved similar or higher accuracy than the widely used k-Wave toolbox. Additionally, UltraWave outperformed k-Wave in terms of computational efficiency with both central processing units (CPUs) and GPUs, particularly when using GPU acceleration for elastic wave simulations. These results demonstrated the potential of UltraWave as a promising tool for biomedical ultrasound simulations.

关键词： Solid modeling Accuracy Ultrasonic imaging Biological system modeling Computational modeling Graphics processing units Media Acoustics Computational efficiency Perfectly matched layers

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi)

Applying CNN Models with Stereo Camera for Object Detection ...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Farrell Putra Harimanto Nurul Hasanah Muhammad Khristamto Aditya Pradana Muhammad Nurul Puji Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Research Center for Smart Mechatronics National Research and Innovation Agency KST Samaun Samadikun Bandung Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%.

关键词： YOLO Accuracy Depth measurement Brightness Cameras Electric vehicles Distance measurement Sensors Convolutional neural networks Autonomous vehicles

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