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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Ankit Wagle Gian Singh Jinghua Yang Sunil Khatri Sarma Vrudhula School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ Dept. of Electrical and Computer Engineering Texas A&M University College Station TX

This paper describes a novel design of a threshold logic gate (a binary perceptron) and its implementation as a standard cell. This new cell structure, referred to as flash threshold logic (FTL), uses floating gate (flash) transistors to realize the weights associated with a threshold function. The threshold voltages of the flash transistors serve as proxy for the weights. An FTL cell can be equivalently viewed as a multi-input, edge-triggered flipflop which computes a threshold function on a clock edge. Consequently it can used in automatic synthesis of ASICs. The use of flash transistors in the FTL cell allows programming of the weights after fabrication, thereby preventing discovery of its function by a foundry or by reverse engineering. This paper focuses on the design and characteristics of the FTL cell. We present a novel method for programming the weights of an FTL cell for a specified threshold function using a modified perceptron learning algorithm. The algorithm is further extended to select weights to maximize the robustness of the design in the presence of process variations. The FTL circuit was designed in 40nm technology and simulations with layout-extracted parasitics included, demonstrate significant improvements in area (79.7%), power (61.1%), and performance (42.5%) when compared to the equivalent implementations of the same function in conventional static CMOS design. Weight selection targeting robustness is demonstrated using Monte Carlo simulations. The paper also shows how FTL cells can be used for fixing timing errors after fabrication.

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An implementation of data exchange in environmental monitoring using authenticated attribute-based encryption with revocation

An implementation of data exchange in environmental monitori...

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2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018

作者： Munsyi Sudarsono, Amang Harun Al Rasvid, M. Udin Dept. of Informatics and Computer Engineering Magister Program of Engineering Technology Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Universitas Muhammadiyah Banjarmasin Banjarmasin Indonesia

ISBN: (纸本)9781538680797

Internet of things era grown very rapidly in Industrial Revolution 4.0, there are many researchers use the Wireless Sensor Network (WSN) technology to obtain the data for environmental monitoring. The data obtained from WSN will be sent to the Data Center, where users can view and collect all of data from the Data Center using end devices such as personal computer, laptop, and mobile phone. The Data Center would be very dangerous, because everyone can intercept, track and even modify the data. Security requirement to ensure the confidentiality all of stored data in the data center and give the authenticity in data has not changed during the collection process. Ciphertext Policy Attribute-Based Encryption (CP-ABE) can become a solution to secure the confidentiality for all of data. Only users with appropriate rule of policy can get the original data. To guarantee there is no changes during the collection process of the data then require the time stamp digital signature for securing the data integrity. To protect the confidentiality and data integrity, we propose a security mechanism using CP-ABE with user revocation and Time Stamp Digital Signature using Elliptic Curve Cryptography (ECC) 384 bits. Our system can do the revocation for the users who did the illegal access. Our system is not only securing the data but also providing the guarantee that is no changes during the collection process of the data from the Data Center. © 2018 IEEE.

关键词： Wireless sensor networks

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10:20-10:30 Ultrasound-based monitoring of fetal lung development predicts the risk for respiratory infections in infants

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Journal of Reproductive Immunology 2023年 158卷 103564-103564页

作者： Zazara, Dimitra E. Schepanski, Steven Giannou, Olympia Pagenkemper, Mirja Ozga, Ann-Kathrin Hecher, Kurt, MD Diemert, Anke Arck, Petra Clara Division for Experimental Feto-Maternal Medicine Department of Obstetrics and Prenatal Medicine University Medical Center of Hamburg-Eppendorf (UKE) Germany University Children's Hospital UKE Hamburg Germany Developmental Neurophysiology Institute of Neuroanatomy UKE Hamburg Germany Computer Engineering & Informatics Dept. Polytechnic School University of Patras Patras Greece Department of Obstetrics and Prenatal Medicine UKE Germany Institute of Medical Biometry and Epidemiology UKE Germany

ProblemInfancy is a window of increased sensitivity to respiratory infections. Besides postnatal environmental exposures, the origin of early-life lung dysfunction and thus, increased risk for respiratory infections may lie in fetal life. However, insights into early indicators and predictors for poor postnatal respiratory health are missing. To address this gap in knowledge, we here aimed to investigate whether a serial sonographic assessment of fetal lung growth can predict the risk for respiratory infections in infancy. Methods of StudyFetal lung growth was serially evaluated using transabdominal sonography in 133 pregnant women within the pregnancy cohort PRINCE (PRenatal determINants of Childrens’s hEalth) study. The lung area was determined by subtracting the cardiac area from the thoracic one at the fourchamber-level at at least three time points during pregnancy and fetal lung growth trajectories were accordingly designed. The number and type of respiratory infections in the first year of life was documented. To examine if the fetal lung growth trajectory can predict the respiratory infection risk in infancy, a Poisson regression model was applied. Using a machine learning approach, a novel model was developed to predict the risk for early-life respiratory infections based on fetal lung growth. ResultsAn enhanced fetal lung growth throughout pregnancy was pinpointed as a predictor for a lower risk for early-life respiratory infections in boys and girls. Male sex was independently linked to an increased risk for respiratory infections in infancy. A highly accurate machine learning model to predict the risk for early-life respiratory infectionsin both boys and girls was designed. Model variables included known risk factors and novel predictors such as the sonographically assessed fetal lung growth trajectory. ConclusionsA serial sonographic assessment of fetal lung growth throughout pregnancy can serve as a predictor for the risk for respiratory infections in

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A Novel Circuit Topology and Its Design for Class-E~2 DC-DC Converter

A Novel Circuit Topology and Its Design for Class-E~2 DC-DC ...

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IEEE Energy Conversion Congress and Exposition

作者： Yusuke Ogi Fumiya Ebihara Xiuqin Wei Hiroo Sekiya Dept. Electrical Electronic and Computer Engineering Chiba Institute of Technology Chiba Japan Dept. Electrical and Electronic Engineering Faculty of Engineering Chiba Institute of Technology Chiba Japan Dept. Applied and Cognitive Informatics Chiba University Chiba Japan

This paper presents a novel class-E 2 dc-dc converter and its design procedure. The proposed converter can maintain high-frequency and high-efficiency operations while realizing low switch-voltage stresses. Additionally, by using the presented design procedure, the proposed converter satisfying the zero-voltage-switching and zero-derivative-switching (ZVS/ZDS) conditions can be implemented without applying any tuning processes. A design example of the proposed class-E 2 converter is shown along with its LTspice simulation and laboratory experiment. The results from the LTspice simulator and laboratory experiment show quantitative agreement with the analytical predictions, which indicates the validity of the proposed topology and design procedure.

关键词： Switches Inverters Switching circuits Resonant frequency DC-DC power converters Inductors Integrated circuits

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Metasurface-coated devices: A new paradigm for energy-efficient and secure 6G communications

arXiv

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arXiv 2020年

作者： Tsiftsis, Theodoros A. Valagiannopoulos, Constantinos Liu, Hongwu Boulogeorgos, Alexandros-Apostolos A. Miridakis, Nikolaos I. The Department of Physics Nazarbayev University Nur-SultanKZ-010000 Kazakhstan The School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China The Department of Digital Systems University of Piraeus Piraeus18534 Greece The Institute of Physical Internet School of Intelligent Systems Science & Engineering Jinan University Zhuhai Campus Zhuhai519070 China The Dept. of Informatics and Computer Engineering University of West Attica Aegaleo12243 Greece

The sixth-generation (6G) era comes with the challenge of offering highly energy-efficient and autonomous communications securely. In this direction, we report energy efficiency (EE), energy harvesting (EH), and secure performance by employing power-collecting metasurface-coated devices capable of supporting ultra-low-power (ULP) transmissions. Contrary to reconfigurable intelligent surfaces (RIS), where the reflected signal can be combined at the receiver by being treated as transmitted from a relay, the proposed metasurface claddings can be deployed at either or both the transmitter and receiver. The passive metasurface-coated devices can achieve ultra-high EE and EH besides the signal detection, combined with an enhanced secrecy rate at the legitimate user and/or improved spying capabilities of the eavesdroppers under ULP transmission. To quantify their efficiency, we provide a holistic model for the utilization of the metasurface shells. Building upon the aforementioned model, preliminary results are presented that reveal the unprecedented superiority of the proposed concept compared to the RIS paradigm. Additionally, we enumerate the main advantages of the new concept and define its role in the 6G era. Finally, possible research directions are discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Network Disruption Prediction Using Naïve Bayes Classifier

Network Disruption Prediction Using Naïve Bayes Classifier

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International Conference of computer and informatics engineering (IC2IE)

作者： Shinta Oktaviana Iklima Ermis Mila Desi Anasanti Jehad Hammad Dept. Computer and Informatics Engineering Politeknik Negeri Jakarta Indonesia Dept. Medicine Imperial College London Section of Genomics of Common Disease UK Dept. of Computer and Information Systems Al-Quds Open University Bethlehem Palestine

ISBN: (纸本)9781728123851

The most crucial challenge of internet service providers is to assure the availability and reliability of their services to their customers. The companies should prevent the customer's complaint by recognizing a potential disruption for the customers, especially in the category `under spec' condition (potentially impaired service). This study proposed and implemented a model using the Naïve Bayes classifier to classify and detect the potential disruption of network services to prevent customer's complaints about their service. The criteria for this model prediction are revenue number of each customer (REVENUE), recurrent disruption value of ODP (N_Q), attenuation value in ODP (OLT), and attenuation value in customer (ONU). The data classified into three classes or conditions, namely GREEN representing no network disruption, YELLOW is representing low-level disruption, and RED representing high-level disruption, which needs more attention to follow up. The result obtained 91.89% accuracy of the model performance using WEKA Tool.

关键词： Attenuation Predictive models Companies Optical network units Training data informatics Optical fibers

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Evaluating deep transfer learning for whole-brain cognitive decoding

arXiv

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arXiv 2021年

作者： Thomas, Armin W. Lindenberger, Ulman Samek, Wojciech Müller, Klaus-Robert Machine Learning Group Dept. of Computer Science and Electrical Engineering Technische Universität Berlin Berlin Germany Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany Stanford Data Science Stanford University StanfordCA United States Dept. of Psychology Stanford University StanfordCA United States Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Dept. of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states. © 2021, CC BY.

关键词： Decoding

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Boundary value analysis testing on augmented reality of indonesian fruit recognition at mekarsari tourist park using cloud method on android mobile devices

Boundary value analysis testing on augmented reality of indo...

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International Conference on Information System, computer Science and engineering 2018, ICONISCSE 2018

作者： Dewi Agushinta, R. Hustinawaty Jatnika, Ihsan Medyawati, Henny Dept. of Information System Faculty of Computer Science and Information Technology Gunadarma University Jl. Margonda Raya No. 100 Pondok Cina Depok West Java16424 Indonesia Dept. of Informatics Engineering Faculty of Industrial Technology Gunadarma University Jl. Margonda Raya No. 100 Pondok Cina Depok West Java16424 Indonesia

Software testing is a process of implementing a program with the aim of finding an error. A good test case is if the test has the possibility of finding an uncovered error. A successful test is if the test finds an error that was not initially found. One of the testing types available is black box testing. This paper proposes testing using black box testing technique. The black box testing method consists of several ways including equivalence partitioning, boundary value analysis, comparison testing, sample testing, robustness testing, and others. Among the many methods of testing, Boundary Value Analysis was chosen in this study. Boundary Value Analysis is a method of testing by determining the value of the lower limit and upper limit of the data that will be tested. This test is performed on the functions of Augmented Reality prototype of Indonesia fruit recognition by using the cloud method on Android mobile devices. From testing the distance of marker objects to Android mobile devices on cloud recognition using an Android camera shows that the higher the augmentable rating of the target image and the more the number of markers features detected, the easier the image will be tracked by AR. If the distance between the camera and the real object gets farther away, then the virtual object cannot be displayed. Testing with mobile devices using HSDPA, 3G and WIFI networks connecting a cloud database server to display virtual objects shows the average results of devices that use WIFI networks provide the fastest performance. © Published under licence by IOP Publishing Ltd.

关键词： Testing

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Adversarial Robustness of Similarity-Based Link Prediction

Adversarial Robustness of Similarity-Based Link Prediction

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IEEE International Conference on Data Mining (ICDM)

作者： Kai Zhou Tomasz P. Michalak Yevgeniy Vorobeychik Dept. of Computer Science and Engineering Washington University in St. Louis St. Louis MO USA Institute of Informatics University of Warsaw Warsaw Poland

Link prediction is one of the fundamental problems in social network analysis. A common set of techniques for link prediction rely on similarity metrics which use the topology of the observed subnetwork to quantify the likelihood of unobserved links. Recently, similarity metrics for link prediction have been shown to be vulnerable to attacks whereby observations about the network are adversarially modified to hide target links. We propose a novel approach for increasing robustness of similarity-based link prediction by endowing the analyst with a restricted set of reliable queries which accurately measure the existence of queried links. The analyst aims to robustly predict a collection of possible links by optimally allocating the reliable queries. We formalize the analyst's problem as a Bayesian Stackelberg game in which they first choose the reliable queries, followed by an adversary who deletes a subset of links among the remaining (unreliable) queries by the analyst. The analyst in our model is uncertain about the particular target link the adversary attempts to hide, whereas the adversary has full information about the analyst and the network. Focusing on similarity metrics using only local information, we show that the problem is NP-Hard for both players, and devise two principled and efficient approaches for solving it approximately. Extensive experiments with real and synthetic networks demonstrate the effectiveness of our approach.

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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