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2024 IEEE International Conference on Big Data, BigData 2024

作者： Zungor, Ozlem Uludag, Yigit Celikel, Ozan Pinarer, Ozgun Galatasaray University Grad. School of Science and Engineering Istanbul Turkey Galatasaray University Dept. Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350362480

The advent of real-time data processing applications has revolutionized monitoring and analysis capabilities across diverse operational domains, including big data and healthcare. This study focuses on the development and evaluation of a prototype application designed for real-time data processing in dynamic environments. The application parses configuration files, processes real-time sensor data and outputs calculated values at user-defined intervals, ensuring accuracy and timeliness in data analysis. Experimental evaluations under varied conditions validate the application's robust performance in managing pipeline operations, computational efficiency, response times and data aggregation precision. Optimizations, including data type adjustments, significantly enhance network communication efficiency and reduce latency, critical for supporting real-time applications. The application's flexibility in user-configured settings for data storage and aggregation proves essential for adapting to specific application requirements. This adaptability is particularly beneficial f or handling the complexities of big data and the sensitivity of healthcare data. Overall, this study contributes a robust solution for real-time data processing and continuous monitoring, demonstrating its feasibility and applicability across dynamic operational environments, including those requiring rigorous data handling and precise analytics. © 2024 IEEE.

关键词： Big Data Analytics in Healthcare Internet of Medical Things (IoMT) Patient Engagement and Data Privacy Predictive Health Analytics User-Centered Design

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Multi-Task Learning with Convolutional Neural Network Approach for Packet Collision Avoidance in 802.11 WLAN 9

Multi-Task Learning with Convolutional Neural Network Approa...

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9th NAFOSTED Conference on Information and computer science, NICS 2022

作者： Putra, Dody Ichwana Pratama, Harry Bintang Nakashima, Tomoki Nagao, Yuhei Kurosaki, Masayuki Ochi, Hiroshi Grad. School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (纸本)9781665454223

Packet collision can degrad. wireless network performance. The IEEE 802.11 Wireless Local Area Network (WLAN) uses the Clear Channel Assessment (CCA) mechanism to monitor channel availability to avoid interference of the presence signal. CCA successfully detects 802.11 signals if it obtains the packet preamble information or detects the threshold ambient power on the channel to determine the channel state. This paper proposes multi-task learning (MTL) with convolutional neural network (CNN) approach to detect WLAN packet formats and modulation types without preamble part information as a supplement to enhance CCA sensitivity. The main advantages of this method over single-task training are high classification accuracy and rapid learning with a lightweight neural network model. Shared knowledge of representation layers, such as model weights or grad.ents, improves the efficiency of training data and reduces redundancy. WLAN signals generated by the Matlab waveform simulator are used to verify the accuracy of the proposed method, which is then implemented on a real-time SDR-based hardware testbed. Although different time offsets affect the classifications, the proposed method proves superior in classifying the packet format and modulation of WLAN signals with an accuracy of 98.93% and 88.53% at SNR = 24 dB, respectively. The proposed method improves channel utilization and throughput of the WLAN network, as demonstrated by an NS-3 simulation. © 2022 IEEE.

关键词： MATLAB

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Real-Time Heart Rate Monitoring via Wi-Fi Signal

Real-Time Heart Rate Monitoring via Wi-Fi Signal

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Solgun, Hasan Ali Ozcan, Alp Pinarer, Ozgun Galatasaray University Grad. School of Science and Engineering Istanbul Turkey Galatasaray University Dept. Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350324457

Due to the health data privacy issues, wearable devices are less useful in the industry and can not reflect their potential power. Besides, wearable health devices bring constraints such as limited energy budget, needs to recharge and impact on daily activities. To recover these problems and fulfill healthcare needs, the recent trends in the literature is to use wireless signal to capture/recognize human activities. When human activity and emotion recognition study fields merging with the machine learning techniques, can offer a innovative systems. In recent years, with the help of wireless sensing mechanisms, this study field have gained more importance. So that, these wireless sensing mechanisms have eliminated the requirement of physical body contact such as sensor devices. Nowadays, wireless sensing technologies are preferred with the easy and cost-effective access to off-the-shelf WiFi-enabled devices. In this research, a human activity and emotion recognition system have proposed with usage of WiFi channel state information(CSI) mechanism that modelled by machine learning algorithms. As a result, with a tolerable error rate, it is possible to distinguish person's heart rate information with a contactless solution via wi-fi signal. © 2023 IEEE.

关键词： BPM calculation channel state information contactless sensing CSI Healthcare IoT wifi sensing wireless signal

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Experimental Evaluation of Rate Adaptation using Deep-Q-Network in IEEE 802.11 WLAN

Experimental Evaluation of Rate Adaptation using Deep-Q-Netw...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Muhammad Harry Bintang Pratama Tomoki Nakashima Yuhei Nagao Masayuki Kurosaki Hiroshi Ochi Grad. School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan

Rate adaptation algorithm has an important role in the Wi-Fi network. It ensures that the nodes transmit at a suitable transmission rate with minimum packet errors on the receiving side. However, there are cases when the existing algorithms fail to adapt to the changes in the communication environment. In this paper, we propose a rate adaptation algorithm using a Deep Q-network (DQN), in which the DQN agent controls the transmission rate of a node in response to the communication environment. We also evaluate the proposed algorithm using the field-programmable gate array (FPGA) and software-defined radio (SDR). The experimental result shows that the proposed algorithm can adaptively select the suitable MCS and maintain the throughput.

关键词： Adaptation models Simulation Throughput Real-time systems Field programmable gate arrays Wireless fidelity IEEE 802.11 Standard

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Optimizing Optical Signal Quality with Deep Learning Dispersion Compensation at Various Distances

Optimizing Optical Signal Quality with Deep Learning Dispers...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Muhammad Harry Bintang Pratama Lina Marlina Muhammad Fajar Faliasthiunus Pradipta Shien-Kuei Liaw Masayuki Kurosaki Hiroshi Ochi Grad. School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan Grad. Institute of Electro-Optical Engineering National Taiwan University of Science and Technology Taipei Taiwan

One of the limiting factors in long-distance communication with optical fiber is dispersion. Dispersion causes optical pulses to broaden as they travel through the fiber, leading to intersymbol interference that ultimately degrad.s the signal quality. To solve this problem, a dispersion compensator is usually applied after a certain distance to bring the broadened signal back to its original form. However, this conventional approach is ineffective in cost and implementation since more compensators should be added for a longer distance. In this paper, we propose a deep learning (DL)-based dispersion compensator as an alternative to the current compensator and supplement the current approach if the distance is very long. The dataset is created from signals obtained through the simulation done in optisystem. Different DL algorithms proposed in this paper can classify the received signals from various distances into binary values without the help of conventional dispersion compensation.

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Enhancing Healthcare Services through User-Centered Data Collection and Analysis

Enhancing Healthcare Services through User-Centered Data Col...

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IEEE International Conference on Big Data

作者： Ozlem Zungor Yigit Uludag Ozan Celikel Ozgun Pinarer Grad. School of Science and Engineering Galatasaray University Istanbul Turkey Dept. Computer Engineering Galatasaray University Istanbul Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The advent of real-time data processing applications has revolutionized monitoring and analysis capabilities across diverse operational domains, including big data and healthcare. This study focuses on the development and evaluation of a prototype application designed for real-time data processing in dynamic environments. The application parses configuration files, processes real-time sensor data and outputs calculated values at user-defined intervals, ensuring accuracy and timeliness in data analysis. Experimental evaluations under varied conditions validate the application’s robust performance in managing pipeline operations, computational efficiency, response times and data aggregation precision. Optimizations, including data type adjustments, significantly enhance network communication efficiency and reduce latency, critical for supporting real-time applications. The application’s flexibility in user-configured settings for data storage and aggregation proves essential for adapting to specific application requirements. This adaptability is particularly beneficial f or handling the complexities of big data and the sensitivity of healthcare data. Overall, this study contributes a robust solution for real-time data processing and continuous monitoring, demonstrating its feasibility and applicability across dynamic operational environments, including those requiring rigorous data handling and precise analytics.

关键词： Data analysis Sensitivity Pipelines Prototypes Medical services Big Data Real-time systems Time factors Monitoring Optimization

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Multi-Task Learning with Convolutional Neural Network Approach for Packet Collision Avoidance in 802.11 WLAN

Multi-Task Learning with Convolutional Neural Network Approa...

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National Foundation for science and Technology Development Conference on Information and computer science (NICS)

作者： Dody Ichwana Putra Harry Bintang Pratama Tomoki Nakashima Yuhei Nagao Masayuki Kurosaki Hiroshi Ochi Grad. School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan

关键词： Wireless LAN Sensitivity Computational modeling Modulation Multitasking Mathematical models Hardware

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Real-Time Heart Rate Monitoring via Wi-Fi Signal

Real-Time Heart Rate Monitoring via Wi-Fi Signal

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IEEE International Conference on Big Data

作者： Hasan Ali Solgun Alp Ozcan Ozgun Pinarer Grad. School of Science And Engineering Galatasaray University Istanbul Turkey Dept. Computer Engineering Galatasaray University Istanbul Turkey

Due to the health data privacy issues, wearable devices are less useful in the industry and can not reflect their potential power. Besides, wearable health devices bring constraints such as limited energy budget, needs to recharge and impact on daily activities. To recover these problems and fulfill healthcare needs, the recent trends in the literature is to use wireless signal to capture/recognize human activities. When human activity and emotion recognition study fields merging with the machine learning techniques, can offer a innovative systems. In recent years, with the help of wireless sensing mechanisms, this study field have gained more importance. So that, these wireless sensing mechanisms have eliminated the requirement of physical body contact such as sensor devices. Nowadays, wireless sensing technologies are preferred with the easy and cost-effective access to off-the-shelf WiFi-enabled devices. In this research, a human activity and emotion recognition system have proposed with usage of WiFi channel state information(CSI) mechanism that modelled by machine learning algorithms. As a result, with a tolerable error rate, it is possible to distinguish person’s heart rate information with a contactless solution via wi-fi signal.

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SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems

arXiv

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arXiv 2023年

作者： Ye-Bin, Moon Hyeon-Woo, Nam Choi, Wonseok Kim, Nayeong Kwak, Suha Oh, Tae-Hyun Dept. of Electrical Engineering POSTECH Pohang Korea Republic of Grad. School of Artificial Intelligence POSTECH Pohang Korea Republic of Dept. of Computer Science and Engineering POSTECH Pohang Korea Republic of

Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synthetic data to equalize the unbalanced distribution of training data. Our experiments demonstrate that, although a domain gap between real and synthetic data exists, training with SYNAuG followed by fine-tuning with a few real samples allows to achieve impressive performance on diverse tasks with different data imbalance issues, surpassing existing task-specific methods for the same purpose. © 2023, CC BY.

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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