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Experiment on Multi-hop LoRa for Tracking Application

International Journal of Sensors, Wireless Communications and Control 2023年第1期13卷 29-39页

作者： Sagala, Albert Lubis, Ramot Silalahi, Gomgom Ginting, Estika V. Simorangkir, Andreas J. D. Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Samosir Indonesia Department of Computer Science Binus University West Jakarta Indonesia

Background: The Internet of Things (IoT) is an embryonic model that converts real-world things (objects) into smarter devices. IoT is applicable to a variety of application domains, including healthcare, smart grid, and agriculture. Low Power Wide Area Network (LPWAN) technology can transmit data over long distances with a low data rate without using the internet. There-fore, this technology is suitable for sending sensor measurement data in Internet of Things (IoT) applications. LPWANs, such as LoRa, use a star topology to reach their coverage to the maximum power that can be transmitted, considering the environmental conditions in the test area and the LoRa parameters used. Whereas for application development in rural areas, there is no internet connection, and often longer coverage is required from the sensor node to the gateway. Objective: This study aims to increase the transmission range from the end node to the gateway by adding an intermediate node. It is used as a relay from the end node to the gateway. Method: Static routing was applied so that any broadcast data from the end node would be for-warded first to the intermediate node and then to the gateway. Results: The LoRa transmission range expanded by changing the topology from peer-to-peer to multi-hop. An intermediate node between the transmitter (end node) and the gateway is available. In the experiment performed, a multi-hop with static routes successfully expanded the distance from 150 meters (single hop) to 250 meters (multi-hop), with a packet delivery ratio of 97%. In this experiment, GPS Ublox Neo was used for the sensor position and was successfully tested on the end node with an accuracy rate of 2.4% for latitude and 0.2% for longitude. Conclusion: The transmission range from the sensor node to the gateway can be extended using static routing by installing an intermediate node between the gateway and the sensor node. The data from the end node is successfully received by the gateway. © 2023 Be

关键词： energy efficiency GPS ublox neo internet of things LoRa tracking LPWAN multi-hop wireless sensor networks

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Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

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Journal of computer and Communications 2024年第2期12卷 116-141页

作者： Marios Vlachos Evangelos Dermatas Institute of Communication and Computer Systems Athens Greece Department of Computer Engineering and Informatics University of Patras Patras Greece

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

关键词： Finger Vein Vessel Enhancement Vessel Network Extraction Non-Uniform Images Binarization Morphological Post-Processing

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Performance Evaluation of Raspberry Pi Pico for Internet of Things and Its Analysis 4

Performance Evaluation of Raspberry Pi Pico for Internet of ...

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Adi, Puput Dani Prasetyo Kristiyanto, Arip Sopandi, Ajang Nirmala, Irma Tahir, Muhlis Wijaya, Etistika Yuni Department of Telecommunication Bandung Indonesia Faculty of Computer Sciences Pamulang University Department of Information System Indonesia University of Muhammadiyah Department of Informatics Engineering Tangerang Indonesia Tanjungpura University Department of Computer Systems West Borneo Pontianak Indonesia Trunojoyo University Madura Department of Informatics Education Indonesia

ISBN: (纸本)9798350378368

The massive development of the Internet of Things to date continues to develop a lot of small devices that are integrated such as the Wireless Sensor Network, several experts have conducted research using simulation and practice of WSN, one of the powerful embedded system hardware is the Raspberry Pi Pico, which is a small-sized Raspberry Pi such as Mini Arduino or Micro Arduino. The purpose of developing research in the field of Embedded Systems is to strive in terms of power consumption and dynamism in research, which can be placed on drones and also in various research ideas such as medical for patient comfort. Raspberry Pi RP2040 microcontroller uses an ARM Cortex-M0+ dual-core processor, Raspberry Pi Pico works on a voltage of 1.8 - 5.5 V DC, and can be handled with a 3.7 V DC battery. With its small dimensions, Raspberry Pi pico can be applied to various applicative research. © 2024 IEEE.

关键词： ARM processors

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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Optimizing Smart City Water Distribution Systems Using Deep Reinforcement Learning 20

Optimizing Smart City Water Distribution Systems Using Deep ...

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20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Zaman, Mostafa Tantawy, Ashraf Abdelwahed, Sherif Virginia Commonwealth University Electrical and Computer Engineering RichmondVA United States School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350331110

Inefficient scheduling in water distribution systems can lead to energy waste, costly overflows, and a system that cannot keep up with demand. Simultaneous real-time management of system components such as pumps and valves to optimize operation in response to demand variations is a challenging task. Recent advances in deep reinforcement learning provides an opportunity to overcome the state explosion problem using function approximation to generalize from a limited interaction with the environment. In this work, we train a Long Short-Term Memory (LSTM) based Reinforcement Learning (RL) agent to optimize the energy usage of a smart water distribution system while maintaining a safe operating envelope. We compare the performance of the RL agent to two agents based on human experience in the domain;a baseline controller that is based on simple operational logic, and a fuzzy logic controller that captures imprecise human requirements. We show that the RL agent outperforms the other agents in terms of energy usage and operational safety, indicating its potential benefits for large-scale smart city systems. Future research work will focus on prioritized large-scale system scheduling to cope with smart city emergency situations. © 2023 IEEE.

关键词： Water distribution systems

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A distributed B+Tree indexing method for processing range queries over streaming data

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Cluster Computing 2024年第2期27卷 1251-1274页

作者： Safaee, Shahab Mirabi, Meghdad Rahmani, Amir Masoud Safaei, Ali Asghar Department of Computer Engineering Faculty of Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Medical Informatics Faculty of Medical Sciences Tarbiat Modares University Tehran Iran

A data stream exhibits as a massive unbounded sequence of data elements continuously generated at a high rate. Stream databases raise new challenges for query processing due to both the streaming nature of data which constantly changes over time and the wider range of queries submitted by the user when compared with the traditional databases. In this paper, we propose a system architecture which includes components for both distributed indexing of streaming data and distributed processing of range queries on streaming data. Instead of creating a large and centralized B+Tree index structure, we create a set of small B+Tree indexes in such a way that a B+Tree index can be created for every partition of streaming data. We also design a distributed range search algorithm which can be used by each individual machine inside a Spark cluster to independently process range queries on each partition of streaming data. By exploiting the proposed system architecture, the process of indexing of streaming data and the process of querying over streaming data can be performed in a distributed and parallel manner. By performing several experiments, we demonstrate that our proposed indexing method is scalable and efficient for processing range queries on streaming data compared to the existing centralized B+Tree indexing methods and therefore, it can be used for applications involving data streams with a large volume of data elements and a large number of range queries. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Query processing

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Large Language Models versus Natural Language Understanding and Generation 27

Large Language Models versus Natural Language Understanding ...

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27th Pan-Hellenic Conference on Progress in Computing and informatics with International Participation, PCI 2023

作者： Karanikolas, Nikitas Manga, Eirini Samaridi, Nikoletta Tousidou, Eleni Vassilakopoulos, Michael Dept. of Informatics and Computer Engineering University of West Attica Athens Greece Dept. of Electrical and Computer Eng. University of Thessaly Volos Greece

ISBN: (纸本)9798400716263

In recent years, the process humans adopt to learn a foreign language has moved from the strict "Grammar -Translation"method, which is based mainly on grammar and syntax rules, to more innovative processes, resulting to the more modern "Communicative approach". As its name states, this approach focuses on the coherent communication with native speakers and the cultivation of oral skills, without taking into consideration, at least at the first stages, the rules that govern the language. The same trend seems to have been applied to the way machinery can be "educated"to comprehend and reproduce the unfamiliar, human language. The "rule based"Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms, on one hand, and the "text based"Large Language Models (LLMs), on the other, are two, analogous to the two human foreign language learning processes, subareas of Natural Language Processing (NLP). This paper presents these two alternative approaches, LLMs (a technology having surfaced as an influential catalyst of NLP, during last years) on the one hand and NLG/NLU on the other, highlighting their applications, their technologies, their capabilities, their differences, their strengths and weaknesses and the challenges they present, contributing to a deeper comprehension of the evolving landscape of Artificial Intelligence and human-computer communication. © 2023 Owner/Author.

关键词： Natural language processing systems

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PToPI: A Comprehensive Review, Analysis, and Knowledge Representation of Binary Classification Performance Measures/Metrics

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SN computer Science 2023年第1期4卷 13页

作者： Canbek, Gürol Taskaya Temizel, Tugba Sagiroglu, Seref Pointr Ankara Turkey Informatics Institute Middle East Technical University Ankara Turkey Computer Engineering Department Gazi University Ankara Turkey

Although few performance evaluation instruments have been used conventionally in different machine learning-based classification problem domains, there are numerous ones defined in the literature. This study reviews and describes performance instruments via formally defined novel concepts and clarifies the terminology. The study first highlights the issues in performance evaluation via a survey of 78 mobile-malware classification studies and reviews terminology. Based on three research questions, it proposes novel concepts to identify characteristics, similarities, and differences of instruments that are categorized into ‘performance measures’ and ‘performance metrics’ in the classification context for the first time. The concepts reflecting the intrinsic properties of instruments such as canonical form, geometry, duality, complementation, dependency, and leveling, aim to reveal similarities and differences of numerous instruments, such as redundancy and ground-truth versus prediction focuses. As an application of knowledge representation, we introduced a new exploratory table called PToPI (Periodic Table of Performance Instruments) for 29 measures and 28 metrics (69 instruments including variant and parametric ones). Visualizing proposed concepts, PToPI provides a new relational structure for the instruments including graphical, probabilistic, and entropic ones to see their properties and dependencies all in one place. Applications of the exploratory table in six examples from different domains in the literature have shown that PToPI aids overall instrument analysis and selection of the proper performance metrics according to the specific requirements of a classification problem. We expect that the proposed concepts and PToPI will help researchers comprehend and use the instruments and follow a systematic approach to classification performance evaluation and publication. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification Knowledge representation Machine learning Performance evaluation Performance measures Performance metrics Periodic table

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago United States University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States University of Oslo Department of Informatics Oslo Norway Illinois Institute of Technology Department of Civil Engineering Chicago United States

ISBN: (纸本)9798350351255

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms - Federated Vehicular Network, NOMA © 2024 IEEE.

关键词： Data privacy

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