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DCSK-based Waveform Design for Self-sustainable RIS-aided Noncoherent SWIPT

IEEE Transactions on Communications 2025年

作者： Mukherjee, Priyadarshi Psomas, Constantinos Krikidis, Ioannis Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This paper investigates the problem of transmit waveform design in the context of a chaotic signal-based self-sustainable reconfigurable intelligent surface (RIS)-aided system for simultaneous wireless information and power transfer (SWIPT). Specifically, we propose a differential chaos shift keying (DCSK)-based RIS-aided point-to-point set-up, where the RIS is partitioned into two non-overlapping surfaces. The elements of the first sub-surface perform energy harvesting (EH), which in turn, provide the required power to the other sub-surface operating in the information transfer (IT) mode. In this framework, by considering a generalized frequency-selective Nakagami-m fading scenario as well as the nonlinearities of the EH process, we derive closed-form analytical expressions for both the bit error rate (BER) at the receiver and the harvested power at the RIS. Our analysis demonstrates, that both these performance metrics depend on the parameters of the wireless channel, the transmit waveform design, and the number of reflecting elements at the RIS, which switch between the IT and EH modes, depending on the application requirements. Moreover, we show that, having more reflecting elements in the IT mode is not always beneficial and also, for a given acceptable BER, we derive a lower bound on the number of RIS elements that need to be operated in the EH mode. Furthermore, for a fixed RIS configuration, we investigate a trade-off between the achievable BER and the harvested power at the RIS and accordingly, we propose appropriate transmit waveform designs. Finally, our numerical results illustrate the importance of our intelligent DCSK-based waveform design on the considered framework. © 1972-2012 IEEE.

关键词： Energy harvesting

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A Three-Step Procedural Paradigm for Domain-Specific Social Media Slang Analytics 2

A Three-Step Procedural Paradigm for Domain-Specific Social ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sundaram, Aishwarya Subramaniam, Hema Ab Hamid, Siti Hafizah Nor, Azmawaty Mohamad Universiti Malaya Institute for Advanced Studies Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia Universiti Malaya Faculty of Education Department of Educational Psychology and Counselling Kuala Lumpur Malaysia

ISBN: (纸本)9798350384277

Social media is a crucial aspect of modern society, shaping global communication and information exchange. Social media slang encompasses informal language expressions, abbreviations, and unconventional words on these platforms, serving purposes such as concise communication and identity establishment. Data analytics from social media slang plays a vital role in providing real-time trends, enhancing sentiment analysis, and improving the accuracy of predictive models by capturing evolving linguistic trends. This study aims to address gaps in existing social media slang analytics by proposing a three-step process for extracting and validating domain-specific social media slang terms, specifically focusing on the domain of anxiety prediction in the context of mental health. The proposed approach involves the identification of relevant users, extraction of social media slang terms, and validation by subject matter experts. A pilot study is conducted in the domain of anxiety prediction, employing a self-prepared questionnaire and involving participants aged 13-14 from schools in Selangor, Malaysia. The pilot study identifies ten social media slang terms associated with anxiety, validated by subject matter experts who are licensed mental health practitioners. The slang terms exhibit varying prevalence levels among adolescents, with some terms absent from popular slang dictionaries like Urban Dictionary. The findings highlight the need for extracting slang terms from pertinent users and subject matter expert validation in incorporating social media slang into more appropriate predictive analytics. © 2024 IEEE.

关键词： Data Analytics

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Latha Mary, S Angel Ulagapriya, K. Poonguzhali, A. Menaha, R. David, Beaulah Priyadharshini, T.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350334050

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection. © 2023 IEEE.

关键词： Optical radar

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

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Procedia computer Science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

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Fingerspelling Classification for Robot Control 22

Fingerspelling Classification for Robot Control

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： McCready, Kevin Coleman, Sonya Kerr, Dermot Siddique, Nazmul Kerr, Emmett Aloimonos, Yiannis Fermüller, Cornelia School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom Department of Electronic and Mechanical Engineering Atlantic Technological University Co.Donegal Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9798331527471

Improvements to human-robot interaction methods could increase the ease of use of robots in manufacturing environments. Many of these environments are noisy and therefore preclude the use of audio communication between humans or in human-robot interactions. Therefore, this paper proposes using a gesture based communication system for robot control. To that end, the VGG16 and VGG19 convolutional neural network (CNN) structures are used for gesture classification along with 3 datasets of American Sign Language (ASL) fingerspelling images. The model performance is evaluated, and modifications made to their parameters to improve performance, before applying them to robot control tasks. The results show that with parameter tuning, test accuracies of up to, 100% are achievable. © 2024 IEEE.

关键词： Convolutional neural networks

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Convolution Processing Unit Featuring Adaptive Precision using Dynamic Reconfiguration 7

Convolution Processing Unit Featuring Adaptive Precision usi...

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： Cain, David Eldash, Omar Khalil, Kasem Bayoumi, Magdy University of Louisiana at Lafayette Department of Electrical and Computer Engineering Louisiana United States The Center for Advanced Computer Studies

ISBN: (纸本)9781665444316

As the demand of low latency machine learning IoT devices has rapidly expanded in the past decade for commercial and industrial applications, cloud computing models have relied on high-speed connectivity which many devices do not have access to. Cloud based solutions are also becoming less attractive for inference solutions with arising privacy and security concerns. Transitioning computation towards edge devices is becoming essential and finding ways to make such computer vision workloads operational on constrained IPs is a key element in this work. As edge devices are limited in processing resources, relying on reconfigurable hardware such as FPGAs will offer an embedded platform to dynamically shape the IPs and cores as needed for each specific application working on it. Convolutional Neural Networks and alike computer vision techniques rely heavily on computation and matrix based operations, this paper proposes a dedicated convolution processing element. The proposed a convolution processing element is implemented on a Pynq-Z2 FPGA that supports Dynamic Partial Reconfiguration between integer point convolution operations of 8, 16, and 32 bit precision. The use of Dynamic Partial Reconfiguration at a processing element level enables the design to be reconfigured 800× more quickly than traditional methods while minimally increasing system overhead. © 2021 IEEE.

关键词： Convolution

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Emergence of composite many-body exciton states in WS2 and MoSe2 monolayers

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Physical Review B 2024年第4期109卷 L041304-L041304页

作者： J. Choi J. Li D. Van Tuan H. Dery S. A. Crooker National High Magnetic Field Laboratory Los Alamos New Mexico 87545 USA Advanced Instrumentation Institute Korea Research Institute of Standards and Science Daejeon 34113 Korea Wuhan National High Magnetic Field Center and School of Physics Huazhong University of Science and Technology Hubei 430074 China Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA

When doped with a high density of mobile charge carriers, monolayer transition-metal dichalcogenide (TMD) semiconductors can host new types of composite many-particle exciton states that do not exist in conventional semiconductors. Such multiparticle bound states arise when a photoexcited electron-hole pair couples not to just a single Fermi sea that is quantum-mechanically distinguishable (as in the case of conventional charged excitons or trions), but rather couples simultaneously to multiple Fermi seas, each having distinct spin and valley quantum numbers. Composite six-particle “hexciton” states were recently identified in electron-doped WSe2 monolayers, but under suitable conditions they should also form in all other members of the monolayer TMD family. Here we present spectroscopic evidence demonstrating the emergence of many-body hexcitons in charge-tunable WS2 monolayers (at the A-exciton) and MoSe2 monolayers (at the B-exciton). The roles of distinguishability and carrier screening on the stability of hexcitons are discussed.

关键词： Excitons Trions Transition metal dichalcogenides Two-dimensional electron system

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Comparative Study of Multilingual Idioms and Similes in Large Language Models

arXiv

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arXiv 2024年

作者： Khoshtab, Paria Namazifard, Danial Masoudi, Mostafa Akhgary, Ali Sani, Samin Mahdizadeh Yaghoobzadeh, Yadollah School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

This study addresses the gap in the literature concerning the comparative performance of LLMs in interpreting different types of figurative language across multiple languages. By evaluating LLMs using two multilingual datasets on simile and idiom interpretation, we explore the effectiveness of various prompt engineering strategies, including chain-of-thought, few-shot, and English translation prompts. We extend the language of these datasets to Persian as well by building two new evaluation sets. Our comprehensive assessment involves both closed-source (GPT-3.5, GPT-4o mini, Gemini 1.5), and open-source models (Llama 3.1, Qwen2), highlighting significant differences in performance across languages and figurative types. Our findings reveal that while prompt engineering methods are generally effective, their success varies by figurative type, language, and model. We also observe that open-source models struggle particularly with low-resource languages in similes. Additionally, idiom interpretation is nearing saturation for many languages, necessitating more challenging evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Large Language Models for Persian ↔ English Idiom Translation

arXiv

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arXiv 2024年

作者： Rezaeimanesh, Sara Hosseini, Faezeh Yaghoobzadeh, Yadollah School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of different prompting methods and LLMNMT combinations on idiom translation has yet to be thoroughly investigated. This paper introduces two parallel datasets of sentences containing idiomatic expressions for Persian→English and English→Persian translations, with Persian idioms sampled from our PersianIdioms resource, a collection of 2,200 idioms and their meanings, with 700 including usage examples. Using these datasets, we evaluate various open and closedsource LLMs, NMT models, and their combinations. Translation quality is assessed through idiom translation accuracy and fluency. We also find that automatic evaluation methods like LLMasajudge, BLEU, and BERTScore are effective for comparing different aspects of model performance. Our experiments reveal that Claude3.5Sonnet delivers outstanding results in both translation directions. For English→Persian, combining weaker LLMs with Google Translate improves results, while Persian→English translations benefit from single prompts for simpler models and complex prompts for advanced ones.1 © 2024, CC BY.

关键词： Neural machine translation

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Cloud-Native Hybrid Deep Learning Pipeline for Network Traffic Classification using Convolutional Recurrent Neural Network

Cloud-Native Hybrid Deep Learning Pipeline for Network Traff...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Satish, E.G. Sudheer Kumar, Komuravelly Adnan, Myasar M. Krithika, M. Sundari, MandapatiVenkata Rama Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Vels Institute of Science Technology and Advanced Studies Department of BCA/IT Pallavaram India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350375442

Identifying encrypted traffic from emerging applications is important and challenging as traditional traffic classification approaches fail to achieve the desired level of accuracy. This necessitates the elaborate strategies to learn about the encrypted network flow. The research focuses on developing a cloud native hybrid Deep Learning (DL) pipeline for Network Traffic Classification (NTC). Initially, the ISCXVPN2016 dataset is considered that consists encrypted and non-encrypted traffic where the raw data is pre-processed with packet length normalization. The features are extracted applying Convolutional Neural Networks (CNNs) to define statistical flow features including inter-arrival time, packet size and flow duration. The data from network traffic is processed and classified using Convolutional Recurrent Neural Network (CRNN) with microservices executed in serverless containers. The hyper parameters in the DL models are then tuned using grid search and the random search algorithms. Experimental results showed that the proposed cloud native CRNN pipeline achieved accuracy of 98.67%, precision of 95.46%, and 94.32% when compared to the existing NTC models Stacked Autoencoders (SAE) based CNN (SAE-CNN) and Autoencoders based CNN (AE-CNN). © 2024 IEEE.

关键词： Convolutional neural networks

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