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检索条件"机构=Dept of Computer and Communication Systems Engineering"

共 374 条记录，以下是71-80 订阅

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Restroom Hygiene Smart Monitoring System Incorporating Lora Technology

Restroom Hygiene Smart Monitoring System Incorporating Lora ...

引用

2022 IEEE Region 10 International Conference, TENCON 2022

作者： Azman, F.I. Saleh, N.L. Ali, A.M. Sali, A. Noor, A.S.M. Universiti Putra Malaysia Systems Engineering Faculty of Engineering Dept of Computer and Communication Selangor Malaysia Institute of Power Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN Selangor Kajang43000 Malaysia Faculty of Engineering Selangor Malaysia Universiti Putra Malaysia Wireless and Photonics Centre of Excellence Faculty of Engineering Selangor Serdang43400 Malaysia

This paper proposed LoRa based system using Wide area networking (WAN) protocol built on top of the LoRa radio modulation operated by a cloud server with a low power battery and operating at long distance. © 2022... 详细信息

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

关键词： Wide area networks

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Deep-Learning-Based VCSEL Transmitter Emulator

Deep-Learning-Based VCSEL Transmitter Emulator

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Stavros Deligiannidis Nikos Argyris Stefanos Dris Dimitrios Kalavrouziotis Paraskevas Bakopoulos Charis Mesaritakis Adonis Bogris Department of Informatics and Computer Engineering University of West Attica Athens Greece NVIDIA Athens Greece Dept. Information and Communication Systems Engineering University of the Aegean Karlovasi Samos Greece

ISBN: (纸本)9798350345995

The design of photonic/opto-electronic structures is a crucial field of research due to the wide range of applications in which these devices are utilized, such as communications, sensing and imaging. The traditional approach to design, which involves numerical simulation and iterative adjustments, is both time-consuming and computationally intensive. The ability to design-emulate photonic structures quickly and efficiently could bring new technologies to market at a faster pace and can also facilitate the process of extracting critical performance metrics from complex fabricated devices. The Vertical Cavity Surface Emitting Laser (VCSEL) and its use as a transmitter broadly used in data center interconnects is an example of a rather complex nonlinear system also exhibiting bandwidth limitation mechanisms attributed to its frequency response and parasitic effects, and thus its numerical simulation based on conventional rate equations can only partially approximate its behavior. When it comes to modelling fabricated devices, such physical models are notoriously difficult to parameterize in order to fit to observed laboratory data; in contrast, the emulation of the experimentally recorded behavior with the use of nonlinear models based on neural networks provides a much more efficient path to modelling, and with potentially higher prediction accuracy [1]. In this work, two types of bidirectional recurrent neural networks (RNN), namely Long Short-Term Memory (bi-LSTM) and Vanilla (bi-VRNN) were used to simulate the dynamic behavior of an experimentally characterized VCSEL-based, PAM-4 transmitter, exhibiting a high prediction accuracy approaching 100%.

关键词： Interpolation Multimode fibers Neural networks Numerical simulation Optical systems Vertical cavity surface emitting lasers

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Convolutional Neural Network-based image tamper detection with Error Level Analysis

Convolutional Neural Network-based image tamper detection wi...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Manjunatha S Swetha M D Rashmi S Ananda Kumar Subramanian Vinoth Kumar V Mallikarjuna Swamy S Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Computer Science and Engineering BNM Institute of Technology Bengaluru India Department of Computer Science and Engineering (Data Science) Dayananda Sagar College of Engineering Bengaluru India Dept of Computational Intelligence School of Computer Science Engineering VIT – University Vellore India School of Computer Science Engineering and Information Systems VIT – University Vellore India Dept of Electronics and Communication Engineering JSS Academy of Technical Education Bengaluru India

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution.

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Complementary Split Square Ring Resonator-Based Double Negative Metamaterial for C, X, and Ku bands Satellite Applications 7

Complementary Split Square Ring Resonator-Based Double Negat...

引用

7th International Conference on Space Science and communication, IconSpace 2021

作者： Hossain, Ismail Samsuzzaman, Md Baharuddin, Mohd Hafiz Bais, Badariah Alam, Touhidul Jit Singh, Mandeep Singh Selangor Malaysia Patuakhali Science And Technology University Dept. Of Computer And Communication Engineering Dumki Patuakhali Bangladesh Universiti Kebangsaan Malaysia Dept Of Electrical Electronic And Systems Engineering Selangor Malaysia

ISBN: (纸本)9781665425230

This study proposed an interlinked coupled split square ring resonator (SSRR) based metamaterial (MTM) unit cell. The double-negative (DNG) metamaterial characteristics are exhibited in C, X, and Ku microwave frequency bands. The proposed MTM unit cell contains six split square ring resonators (SSRR) where a horizontal metal bar is interlinked between the fourth and fifth SSRR and a vertical metal bar interlinks between the third and fifth SSRR. The perfect electric conductor (PEC) and Rogers-RT 5880 are used in the resonator and dielectric layer to design the MTM unit cell. The unit cell has a dimension of 10×10×1.57 mm3, and the thickness of the resonator layer and dielectric layer is 0.04 mm, 1.57 mm, respectively. The computer Simulation Technology (CST Microwave Studio 2019) software is used to extract effective parameters then the finite element method (FEM) is used to verify the unit cell's performance. Due to symmetrical structure, negative permittivity, negative permeability, and negative refractive are achieved. As a result, the proposed unit cell can improve microwave devices' performance for C, X, and Ku bands, particularly satellite and radar communications. © 2021 IEEE.

关键词： Metamaterials

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System

Leveraging Artificial Intelligence for Enhanced Data Generat...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Srijita Bandopadhyay Srimonti Dutta Imran Haider Bhavaraju Anuraag Jerry Zhu Saad Ahmed Bazaz Dept. of Electronics and Communication Engineering University of Engg. & Management Kolkata India School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering Vellore Institute of Technology Vellore India Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the β- VAE filtered approach employed in previous literature.

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Trusted Access to 6G Testbeds Through a Security Intent-Driven Software-Defined Perimeter Framework 29

Trusted Access to 6G Testbeds Through a Security Intent-Driv...

引用

29th IEEE International Workshop on computer Aided Modeling and Design of communication Links and Networks, CAMAD 2024

作者： Fernandez-Martinez, Carolina Bikos, Anastasios Verikoukis, Christos Siddiqui, Shuaib i2CAT Foundation Barcelona08034 Spain Universitat Pompeu Fabra Dept. of Information and Communication Technologies Barcelona08018 Spain Iquadrat Informatica Sl Barcelona08006 Spain Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona08034 Spain Industrial Systems Institute Athena Research Center Patras26504 Greece University of Patras Dept. of Computer Engineering and Informatics Patras26504 Greece

ISBN: (纸本)9798350377644

Parallel to the evolution of recent trends in the cybersecurity industry and the increase of cyberattacks in the last few years, there is renovated interest on the application of software-defined techniques to enforce best security practices, such as in the Software-Defined Perimeter specification and the Zero Trust architectures. This work proposes a software-defined network segmentation and access control framework aligned with these principles. This solution captures minimal security requirements from testbed operators to establish ad-hoc, isolated connectivity and access control for end-users (experimenters) interacting with exposed endpoints from the exposed services in the cloud testbeds. The elicited architecture, design decision and initial implementation are introduced. © 2024 IEEE.

关键词： Testbeds

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Multi-Task Bias-Variance Trade-Off Through Functional Constraints

Multi-Task Bias-Variance Trade-Off Through Functional Constr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Juan Cerviño Juan Andrés Bazerque Miguel Calvo-Fullana Alejandro Ribeiro Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA Dept. of Electrical and Computer Engineering University of Pittsburgh Pittsburgh USA Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain

Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is to exploit the intrinsic similarity across data sources to aid in the learning process for each individual domain. In this paper we draw intuition from the two extreme learning scenarios – a single function for all tasks, and a task-specific function that ignores the other tasks dependencies – to propose a bias-variance trade-off. To control the relationship between the variance (given by the number of i.i.d. samples), and the bias (coming from data from other task), we introduce a constrained learning formulation that enforces domain specific solutions to be close to a central function. This problem is solved in the dual domain, for which we propose a stochastic primal-dual algorithm. Experimental results for a multi-domain classification problem with real data show that the proposed procedure outperforms both the task specific, as well as the single classifiers.

关键词： Soft sensors Signal processing algorithms Signal processing Multitasking Acoustics Classification algorithms Task analysis

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Felix Dobslaw Robert Feldt Dept. of Communication Quality Mngmt. and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

This study examines to what extent the testing of traditional software components and machine learning (ML) models fundamentally differs or not. While some researchers argue that ML software requires new concepts and perspectives for testing, our analysis highlights that, at a fundamental level, the specification and testing of a software component are not dependent on the development process used or on implementation details. Although the software engineering/computer science (SE/CS) and Data Science/ML (DS/ML) communities have developed different expectations, unique perspectives, and varying testing methods, they share clear commonalities that can be leveraged. We argue that both areas can learn from each other, and a non-dual perspective could provide novel insights not only for testing ML but also for testing traditional software. Therefore, we call upon researchers from both communities to collaborate more closely and develop testing methods and tools that can address both traditional and ML software components. While acknowledging their differences has merits, we believe there is great potential in working on unified methods and tools that can address both types of software.

关键词： Software testing Conferences Machine learning Software Testing

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Implementing AI-driven Diagnostic Decision Support systems for Smart Healthcare

Implementing AI-driven Diagnostic Decision Support Systems f...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Anuteja Reddy Neravetla Varun Kumar Nomula Abdul Sajid Mohammed S. Dhanasekaran Department of Information Technology University of the Cumberlands Kentucky USA Department of Analytics/Industrial & Systems Engineering Georgia Institue of Technology USA Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky USA Department of Electronics and Communication engineering Sri Eshwar College of engineering Coimbatore

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Clever healthcare has turned out to be an increasingly more crucial assignment inside the area of healthcare due to its capability to decorate affected person care transport. Using AI-driven diagnostic decision guide systems is one way to permit more green, correct, and regular healthcare decisions. The structures use records from clinical information to growth predictive fashions that may be used to help the analysis of numerous illnesses. With an AI-driven choice guide device, healthcare carriers can successfully select capability diagnoses based on an affected person’s medical information and other elements. The system also can offer hints for treatment plans primarily based on the analysis. AI-pushed structures can also provide actual-time remarks to clinicians, assisting them in improving accuracy and decreasing scientific mistakes. Via the use of AI-pushed preference help systems, healthcare carriers can optimize healthcare shipping, major to better affected person results.

关键词： Decision support systems Accuracy Costs Medical services Clinical diagnosis Medical diagnostic imaging

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Private Balance-Checking on Blockchain Accounts Using Private Integer Addition

Private Balance-Checking on Blockchain Accounts Using Privat...

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IEEE International Symposium on Information Theory

作者： Birenjith Sasidharan Emanuele Viterbo Dept. of Electronics &amp Communication Engineering Govt. Engineering College Trivandrum India Dept. of Electrical and Computer Systems Engineering Monash University Clayton Australia

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

A transaction record in a sharded blockchain can be represented as a two-dimensional array of integers with row-index associated to an account, column-index to a shard and the entry to the transaction amount. In a blockchain-based cryptocurrency system with coded sharding, a transaction record of a given epoch of time is encoded using a maximum-distance-separable code considering the entries as finite-field symbols. Each column of the resultant coded array is then stored in a server. In this paper, we propose a privacy-preserving multi-round protocol that allows a remote client to retrieve from a coded blockchain system the sum of transaction amounts belonging to two different epochs of time, but to the same ac-count. At the core of the protocol lies an algorithm for a remote client to privately compute a non-linear function referred to as integer addition of two finite-field symbols representing integer numbers, in the presence of curious-but-honest adversaries. Applying it to balance-checking in a cryptocurrency system, the protocol guarantees information-theoretic privacy on account number and shard number thereby ensuring perfect user anonymity, and also maintains confidentiality of half of the input bits on average. The protocol turns out to be a useful primitive for balance-checking in lightweight clients of a PolyShard-ed blockchain.

关键词： Privacy Protocols Codes Symbols Blockchains Cryptocurrency Servers

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