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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mercy, Immaculate Nainwal, Ankita Balasubramani, R. Nandankar, Praful V. Parikh, Swapnil Taqui, Syed Noeman Department of Computer Applications Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Karnataka Nitte574110 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat Waghodia India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Effective communication between autonomous vehicles is essential for ensuring their safe and efficient operation. This research introduces Parr-OrAGNet, a novel approach that addresses the limitations of traditional communication methods in IoT automotive networks. Parr-OrAGNet integrates Macro-Micro Depth Normalization (MaMiDN) for accurate data normalization, Orientation-Aware Graph Neural Network (OrAGNet) for enhanced neural network modeling, and Parrot Optimizer (ParrOpt) for optimization. This combined approach effectively reduces packet loss, communication overhead, and latency. The evaluation results demonstrate significant improvements in communication efficiency, with a 97% reduction in packet loss and a 95% decrease in communication overhead. Additionally, Parr-OrAGNet achieves exceptional performance metrics in terms of accuracy, precision, recall, and computational efficiency. This research contributes to the advancement of autonomous vehicle communication by establishing a new benchmark for performance and scalability. Parr-OrAGNet offers a promising solution for addressing the challenges of IoT automotive networks and paving the way for the realization of fully autonomous driving. © 2024 IEEE.

关键词： Macroinvertebrates

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Design and Performance Evaluation of a Standalone MQTT for Sensor Networks (MQTT-SN) Broker

Design and Performance Evaluation of a Standalone MQTT for S...

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IEEE International Workshop on Factory Communication Systems (WFCS)

作者： Michael Nast Frank Golatowski Dirk Timmermann Institute of Applied Microelectronics and Computer Engineering University of Rostock Rostock Germany

The Message Queuing Telemetry Transport (MQTT) protocol is a de-facto standard for machine-to-machine (M2M) communication in the Internet of Things (IoT) domain. However, since it is limited to TCP at transport layer, it is not possible to run MQTT over UDP, which would be desirable for real-time capable publish/subscribe. MQTT for Sensor Networks (MQTT-SN), which is a lightweight version of MQTT, allows the use of UDP, however, is strongly coupled to MQTT by the current specification and cannot be operated independently of it. In this work, we therefore propose a standalone broker implementation of MQTT-SN. In initial measurements, we can show that our approach is much more performant than the approach described in the specification and that our implementation compares very well with the protocols CoAP Pub/Sub and MOTT.

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Thin ceramic PZT dual-and multi-frequency pMUT arrays for photoacoustic imaging

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Microsystems & Nanoengineering 2022年第6期8卷 23-34页

作者： Qincheng Zheng Haoran Wang Hao Yang Huabei Jiang Zhenfang Chen Yao Lu Philip X.-L.Feng Huikai Xie School of Integrated Circuits and Electronics Beijing Institute of Technology(BIT)100081 BeijingChina Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA Department of Medical Engineering University of South FloridaTampaFL 33620USA MEMS Engineering and Materials Inc. SunnyvaleCA 94086USA BIT Chongqing Institute of Microelectronics and Microsystems 400030 ChongqingChina

Miniaturized ultrasonic transducer arrays with multiple frequencies are key components in endoscopic photoacoustic imaging(PAI)systems to achieve high spatial resolution and large imaging depth for biomedical *** this article,we report on the development of ceramic thin-film PZT-based dual-and multi-frequency piezoelectric micromachined ultrasonic transducer(pMUT)arrays and the demonstration of their PAI *** chips sized 3.5mm in length or 10mm in diameter,square and ring-shaped pMUT arrays incorporating as many as 2520 pMUT elements and multiple frequencies ranging from 1 MHz to 8 MHz were developed for endoscopic PAI *** ceramic PZT with a thickness of 9μm was obtained by wafer bonding and chemical mechanical polishing(CMP)techniques and employed as the piezoelectric layer of the pMUT arrays,whose piezoelectric constant d_(31)was measured to be as high as 140 pm/*** from this high piezoelectric constant,the fabricated pMUT arrays exhibited high electromechanical coupling coefficients and large vibration *** addition to electrical,mechanical,and acoustic characterization,PAI experiments with pencil leads embedded into an agar phantom were conducted with the fabricated dual-and multi-frequency pMUT *** signals were successfully detected by pMUT elements with different frequencies and used to reconstruct single and fused photoacoustic images,which clearly demonstrated the advantages of using dual-and multi-frequency pMUT arrays to provide comprehensive photoacoustic images with high spatial resolution and large signal-to-noise ratio simultaneously.

关键词： ceramic piezoelectric dual

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A SystemC Model of the IEEE 802.11 EDCA Protocol for Virtual Prototyping

A SystemC Model of the IEEE 802.11 EDCA Protocol for Virtual...

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2021 International Symposium on Networks, computers and Communications, ISNCC 2021

作者： Beichler, Benjamin Rethfeldt, Michael Raddatz, Hannes Steffen, Lukas Timmermann, Dirk Haubelt, Christian Institute Of Applied Microelectronics And Computer Engineering University Of Rostock Germany

ISBN: (纸本)9780738113166

Due to the increasing complexity of distributed embedded systems, the development process demands early substantiation of design decisions for the targeted system. Virtual prototypes enable an early validation of hardware/software systems and they have proven to be very beneficial for target software development. However, these prototypes often do not include models for inter-device communication aspects of distributed systems. Especially the use of wireless communication has a significant impact on the overall system behavior and performance. This paper introduces a SystemC model of an essential part of IEEE 802.11 Wireless LAN communication, the Enhanced Distributed Channel Access (EDCA) protocol. In comparison to widely used network simulations, the modeling with SystemC promises an easier integration into existing virtual prototypes. EDCA is the main part of the lower medium access layer (MAC) of IEEE 802.11 and interfaces to the physical-layer communication as well as to non-time-critical higher MAC-layer functions. We conducted experiments with an additional simple physical communication channel model to compare our solution in terms of precision and performance to the well-established network simulator ns-3. We show that our model differs by $\approx 0.3 \%$ of retransmitted frames and an accompanied difference of the overall simulated transmission time of $\approx 0.45 \%$. It meets the overall expectations for the stochastic EDCA protocol. Moreover, the presented model has a significantly better simulation performance with $3 \cdot 10^{-5}$ s wall-clock time per frame, which is at least two orders of magnitude better than with the compared ns-3 simulation. © 2021 IEEE.

关键词： Stochastic systems

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhicheng Feng, Shibo Zhang, Zhong Xiao, Xi Gao, Xingyu Zhao, Peilin Shenzhen International Graduate School Tsinghua University China Tencent AI Lab China School of Computer Science and Engineering Nanyang Technological University Singapore Ministry of Education China Institute of Microelectronics Chinese Academy of Sciences China

The superior generation capabilities of Denoised Diffusion Probabilistic Models (DDPMs) have been effectively showcased across a multitude of domains. Recently, the application of DDPMs has extended to time series generation tasks, where they have significantly outperformed other deep generative models, often by a substantial margin. However, we have discovered two main challenges with these methods: 1) the inference time is excessively long;2) there is potential for improvement in the quality of the generated time series. In this paper, we propose a method based on discrete token modeling technique called Similarity-driven Discrete Transformer (SDformer). Specifically, SDformer utilizes a similarity-driven vector quantization method for learning high-quality discrete token representations of time series, followed by a discrete Transformer for data distribution modeling at the token level. Comprehensive experiments show that our method significantly outperforms competing approaches in terms of the generated time series quality while also ensuring a short inference time. Furthermore, without requiring retraining, SDformer can be directly applied to predictive tasks and still achieve commendable results. © 2024 Neural information processing systems foundation. All rights reserved.

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A GPGPU-Based Algorithm Acceleration System for ECG Signal Processing 7

A GPGPU-Based Algorithm Acceleration System for ECG Signal P...

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7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Huang, Tianyu Wang, Zhijiong Hu, Guannan Pun, Sio Hang Li, Hung Chun Vai, Mang I Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Zhuhai China Institute of Microelectronics Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China

ISBN: (纸本)9798331529314

Electrocardiogram (ECG) is an important non-invasive technique for diagnosing cardiovascular diseases (CVD). After acquiring the patients' raw ECG signal data, signal processing is essential for the diagnosis. Conventional ECG signal processing is usually performed serially, which takes a long time to process signals with large amounts of data. In this study, we have proposed a GPGPU-based algorithm acceleration system to improve the efficiency of ECG signal processing. This system utilizes OpenCL for parallel algorithm development on GPGPU. GPGPU executes certain steps in the QRS complex detection algorithm via parallel computing, thereby accelerating the algorithmic process. Experimental results showed that the system achieved a speedup of 1.279 compared to serial computing when processing 39 ECG signal sequences in parallel. In addition, our system has been equipped with a progressive transmission mechanism and a function that intelligently determines whether an algorithm can be accelerated. These features make this system practical not only in biomedical signal processing but also in engineering and research fields with large amounts of data. © 2024 IEEE.

关键词： Noninvasive medical procedures

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IMPLICATIONS OF NOISE IN RESISTIVE MEMORY ON DEEP NEURAL NETWORKS FOR IMAGE CLASSIFICATION

arXiv

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arXiv 2024年

作者： Emonds, Yannick Xi, Kai Fröning, Holger Computing Systems Group Institute of Computer Engineering Heidelberg University Germany Key Laboratory of Microelectronics Device & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences China

Resistive memory is a promising alternative to SRAM, but is also an inherently unstable device that requires substantial effort to ensure correct read and write operations. To avoid the associated costs in terms of area, time and energy, the present work is concerned with exploring how much noise in memory operations can be tolerated by image classification tasks based on neural networks. We introduce a special noisy operator that mimics the noise in an exemplary resistive memory unit, explore the resilience of convolutional neural networks on the CIFAR-10 classification task, and discuss a couple of countermeasures to improve this resilience. © 2024, CC BY-SA.

关键词： Image classification

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IEEE 11073 SDC for Pandemics like COVID-19: Example Implementation of an Isolation Room

IEEE 11073 SDC for Pandemics like COVID-19: Example Implemen...

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作者： Kasparick, Martin Schackmann, Dennis Klotz, Tobias Ritz, Fabian Golatowski, Frank Rethfeldt, Michael Neumuth, Thomas Timmermann, Dirk Rockstroh, Max Institute of Applied Microelectronics and Computer Engineering University of Rostock Richard-Wagner-Straße 31 Rostock Germany ICCAS University of Leipzig Leipzig Germany Drägerwerk AG and Co. KGaA Lübeck Germany Institute of Applied Microelectronics and Computer Engineering University of Rostock Rostock Germany

Treating patients in an intensive care unit (ICU) isolation room is full of challenges for the caregivers and the patients. While this is known for years, the COVID-19 pandemic has highlighted these challenges as if under a burning glass – intensified by a high number of patients at the same time. The risk of infection for the caregivers is very high inside the isolation room and the time spent for putting on and taking off personal protective equipment (PPE) is enormous. Thus, every treatment that can be performed from the anteroom of the isolation room at the same quality level as if being inside the isolation room is a potential building block for solving the challenges. However, data like vital signs have to be provided to the anteroom, alarms have to be transferred to the right caregiver at the right place, and controlling medical device parameters from the anteroom are prerequisites. The new IEEE 11073 Service-oriented Device Connectivity (SDC) series of standards is a key technology to realize this. Therefore, this paper presents an example implementation of IEEE 11073 SDC-based ICU isolation rooms by means of a demonstrator. Such systems can significantly reduce the amount of time the caregivers have to spend in the isolation room and reduce the number of clothing (PPE) changes while providing care at a level of a non-isolation room. As a midterm goal these are steps for the vision of a "silent/quiet ICU". © 2022 The Author(s), published by De Gruyter.

关键词： COVID-19

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Developing Deep Learning Model for Abnormal Heartbeat Detection in Electrocardiography Signals 2

Developing Deep Learning Model for Abnormal Heartbeat Detect...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Ramalakshmi, V. Garg, Neha Soudagar, Manzoore Elahi M. Vekariya, Daxa Patil, Harshal Arun Shalin, L.V. Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of CSE Andhra Pradesh Vijayawada521225 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat 391760 India Computer Science and Engineering Maharashtra Pune412115 India Bannari Amman Institute of Technology Department of Information Technology Tamil Nadu Erode638401 India

ISBN: (纸本)9798350327533

The electrocardiogram (ECG) is an essential instrument used by physicians that provides information about cardiovascular health and is the gold standard for detecting abnormal heartbeats. Traditional medical tests have limitations, which include delayed diagnoses and a high potential for misdiagnosis, especially when after the attention to cardiovascular health grows. Given the importance of early detection, medication, and rehabilitation for cardiovascular diseases (CVD), the rate of misinterpreting must be reduced. This research study develops a CNN-BLSTM (Convolutional Neural Network and Bidirectional Long Short-Term Memory), hybrid Deep Learning (DL) model for categorizing both normal and abnormal cardiac data. The proposed technique is evaluated using ECG signals from the MIT-BIH arrhythmia database, which comprises both normal and five types of abnormal signals. Further, this research study compares the performance of our model to that of existing transfer learning models such as ResNet-50 and AlexNet. With an overall accuracy of 98.11%, the proposed CNN-BLSTM model surpasses both ResNet-50 and AlexNet. Furthermore, the model delivers the greatest prediction scores for each type of heartbeat classification, indicating its effectiveness in accurate and comprehensive heartbeat signal analysis. © 2024 IEEE.

关键词： Cardiovascular Disease Deep Learning Electrocardiogram Heartbeat Normalization

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A 23-nA Quiescent Current Output-Capacitorless LDO Regulator for IoT Devices 15

A 23-nA Quiescent Current Output-Capacitorless LDO Regulator...

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Zhou, Shengnan Mao, Xiangyu Huang, Cheng Martins, Rui P. Lu, Yan University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics and FST-DECE China Iowa State University Department of Electrical and Computer Engineering Ames United States

ISBN: (纸本)9798350312980

This paper presents an ultra-low quiescent current output-capacitorless low dropout regulator (OCL-LDO) with fast transient response. We design a rail-to-rail error amplifier with improved adaptive biasing, which provides a limited bias current at heavy load for better load regulation. The buffer with adaptive biasing and almost zero input-output voltage shift and embedded body-bias, combines the error amplifier and the gate of MP. The proposed LDO is designed in a 65-nm CMOS process. Simulation result shows that the proposed LDO consumes a quiescent current of only 23 nA at a supply voltage of 0.8 V. The voltage undershoot is 98 mV during a 20-mA maximum load transient. And we obtain a FOMT of 0.00023 ps. © 2023 IEEE.

关键词： adaptive biasing internet of things Output-capacitorless LDO rail-to-rail buffer ultra-low quiescent current

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