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Analysis of the acoustoelectric response of SAW gas sensors using a COM model

Microsystems & Nanoengineering 2024年第3期10卷 87-96页

作者： Yang Yuan Tao Yang Xi Chen Linglang Yu Xiaoxiao Hou Guangzu Zhang Wen Dong Zixiao Lu Honglang Li Leonhard Reindl Wei Luo School of Integrated Circuits Huazhong University of Science and Technology430074 WuhanPeople’s Republic of China CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology100190 BeijingPeople’s Republic of China Department of Microsystems Engineering Laboratory for Electrical Instrumentation and Embedded SystemsUniversity of Freiburg79110 FreiburgGermany Research Institute of Huazhong University of Science and Technology in Shenzhen 518057 ShenzhenPeople’s Republic of China

Surface acoustic wave(SAW)gas sensors based on the acoustoelectric effect exhibit wide application prospects for in situ gas ***,establishing accurate models for calculating the scattering parameters of SAW gas sensors remains a ***,we present a coupling of modes(COM)model that includes the acoustoelectric effect and specifically explains the nonmonotonic variation in the center frequency with respect to the sensing film’s sheet *** sensing parameters of the gas sensors,including the center frequency,insertion loss,and phase,were experimentally compared for accuracy and ***,the frequency of the phase extremum(FPE)shift was determined to vary monotonically,and the range of selectable test points was wide,making the FPE an appropriate response parameter for leveraging in SAW gas *** simulation results of the COM model were highly consistent with the experimental *** study is proposed to provide theoretical guidance for the future development of gas SAW sensors.

关键词： COM SAW establishing

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Supervised Local Training With Backward Links for Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第2期5卷 854-867页

作者： Guo, Wenzhe Fouda, Mohammed E. Eltawil, Ahmed M. Salama, Khaled Nabil King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia University of California Center for Embedded and Cyber-Physical Systems IrvineCA92612 United States

The restricted training pattern in the standard backpropagation (BP) requires end-to-end error propagation, causing large memory costs and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacles by completely cutting off the backward path between modules and isolating their gradients. These methods prevent information exchange between modules and result in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces intermodule backward dependence and facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, our method can surpass the conventional greedy local training method by 6.45% in accuracy in ResNet32 classifying CIFAR100 and recent work by 2.58% in ResNet110 classifying STL-10 with much lower complexity. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms toward better efficiency for real-time learning applications. © 2023 IEEE.

关键词： Deep neural networks

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IoT monitoring and control system of Distribution Transformers in Rwanda 23

IoT monitoring and control system of Distribution Transforme...

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12th International Conference on Software and Computer Applications, ICSCA 2023

作者： Bimenyimana, Emmanuel Nsengiyumva, Philibert Ngoga, Said R. University of Rwanda College of Science and Technology African center of Excellency in Internet of Things Embedded Computing Systems Rwanda University of Rwanda College of Science and Technology Rwanda

ISBN: (纸本)9781450398589

In developing countries, many customers do not get good quality of electricity power supply due to frequent and prolonged power fluctuations/cuts. Distribution transformer (DT) is a service transformer that provides the final voltage transformation in the electric power distribution system, stepping down the voltage to the level used by the customers. Monitoring and control of DT (as a crucial and expensive asset in the network) is a key enabler of power stability to consumers. A power utility is said to be a business oriented with good image representation if it delivers a reliable and affordable electricity. Modern technologies such as the Internet of Things (IoT) offer a wide range of applications in the energy sector to smoothly monitor, control and optimize processes. Currently, many energy companies in developing countries are not yet implementing the remote system to control and monitor the secondary side of DTs and timely get the notifications of fluctuations/abnormalities occurred on those DTs. That is why it is still challenging and time consuming to intervene urgently and do the necessary actions to prevent severe and prolonged power cuts/fluctuations and safeguard the damage of DTs themselves with customer’s appliances connected on those DTs. The secondary side of DT is the one connected directly and supply power to the customers. For this reason, we developed an affordable IoT system that automatically detects the abnormalities/fluctuations of three core technical parameters of DT (which are voltage, current and temperature) using current sensors, voltage sensors, and temperature sensor with ATmega 328P Microcontroller to collect and process data from sensors connected to DT system. Once one or all of those technical parameters become abnormal, the system cut off automatically the secondary side of DT in 2 seconds to isolate and protect the customers’ load with safeguarding DT itself using a power relay. At the same time, GSM/GPRS module uploads the sen

关键词： Internet of things

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Towards Sustainable Energy Communities: Local Optimization of Battery Usage in P2P Energy Trading

Towards Sustainable Energy Communities: Local Optimization o...

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2023 IEEE International Conference on Omni-Layer intelligent systems, COINS 2023

作者： Grob, Christoph Gehring, Florian Bringmann, Oliver FZI Research Center for Information Technology Karlsruhe Germany University of Tübingen Faculty of Science Embedded Systems Group Department of Computer Science Germany

ISBN: (纸本)9798350346473

As renewable energy resources are integrated into the power grid to address the global energy and climate crisis, local energy markets have emerged as a possible solution. In such markets, peers in a smart grid trade locally generated energy. To reduce the stress on the grid caused by the volatility of renewable energy sources, households use electric batteries to maximize their self-consumption of generated energy. In this paper, we propose a peer-To-peer trading platform that integrates batteries into the distributed energy management trading system. We show that local optimization of single households not only benefits the individual households in terms of increased self-consumption and financial gains but also has an overall positive impact on the energy community compared to a simple battery implementation. © 2023 IEEE.

关键词： Smart power grids

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Water Quality Monitoring and Controlling systems for Aquaculture 1

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The 4th International Conference on Data science, Machine Learning 2022

作者： Arabelli, Rajeshwarrao Bernatin, T. Department of ECE Sathyabama Institute of Science and Technology Tamilnadu Chennai India Center for Embedded Systems and IoT Department of ECE SR University Telangana Warangal India

ISBN: (数字)9789819920587

ISBN: (纸本)9789819920570

Due to large increases in demand for fish and seafood around the world, aquaculture has been a rapidly expanding sector. Shrimp, fish, and other aquatic crops are grown in aquaculture, where the water’s qualities determine the crop. Dissolved oxygen, salinity, temperature, alkalinity, turbidity, hardness, pH, ammonia, water level, and nutrient levels are a few water quality factors that should be kept at ideal levels to enhance yield. Depending on the circumstances, these characteristics might change drastically throughout the day. In order to fully exploit their potential, these factors must be regulated at high frequencies. This paper affords an in-depth assessment of the diverse strategies utilized in aquaculture to Detect and manage water quality. This survey highlights the research gap on this area and presents higher scope for superior work. Keywords: Aquaculture, Water quality index, Water quality monitoring, Wireless Sensor Net-works. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Distilled Neural Network for Clothes Type Identification 20

Distilled Neural Network for Clothes Type Identification

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20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Chongpiphattanasiri, Teerapon Wickramatilake, Pramod Moonrinta, Jednipat Ekpanyapong, Mongkol Dailey, Matthew N. Asian Institute of Technology Microelectronics and Embedded Systems Pathumthani Thailand Asian Institute of Technology Artificial Intelligence Center Pathumthani Thailand Asian Institute of Technology Computer Science and Information Management Pathumthani Thailand

ISBN: (纸本)9798350310467

Diverse applications such as criminal forensics and retail customer market segmentation would benefit from the ability to track individuals across multiple cameras. Clothing information is one of the keys to this capability when other identifying information such as the face cannot be reliably extracted. To quickly search for a criminal suspect or re-identify individuals across time and space, filtering by clothing over a given set of cameras for a given time range can dramatically narrow the search. To achieve this capability, we propose a training pipeline that adapts a knowledge distillation neural network to develop a lightweight but effective deep learning model for classifying clothing. The proposed training pipeline produces a small model that achieves 78.70% clothes type classification accuracy on the DeepFashion2 benchmark dataset. An experimental inference time of 0.019 per image on a Nvidia RTX 2070 Super GPU demonstrates that the system has potential for deployment over large-scale camera sensor networks. We expect the new approach to accelerate criminal investigations and enable other analytics requiring reidentification of individuals across multiple cameras at scale. © 2023 IEEE.

关键词： Pipelines

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Application of Machine Learning in Lifestyle: Weight-In Image Classification using Convolutional Neural Networks

Application of Machine Learning in Lifestyle: Weight-In Imag...

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2022 IEEE International Conference on Agents, ICA 2022

作者： Asawaponwiput, Warisara Sriiesaranusorn, Panyawut Mohchit, Thawat Thatphithakkul, Nattanun Surangsrirat, Decho Information and Communication Technology for Embedded Systems Kasetsart University Bangkok Thailand Assistive Technology and Medical Devices Research Center National Science and Technology Development Agency Pathum Thani Thailand

ISBN: (数字)9781665469364

ISBN: (纸本)9781665469364

Nowadays, people are increasingly concerned for their health as being healthy is regarded as a profitable investment. Obesity is one of the most common health problems that leads to multiple diseases. We work with the team that developed a mobile application to encourage users to change their eating and activity behaviors to improve their health based on a virtual competition platform. Participants are required to upload a weight-in photo to verify their weight before the challenge. Manually verifying these images can be time-consuming and error-prone due to the large number of images in each competition. In this study, we proposed an image classification approach to help screen incorrect images of the weight-in photo for the virtual competition. The image augmentation techniques were applied to the training images before being input into the classification model. Since the goal is to deploy the model in a mobile application, the suitable model must be small and efficient enough for use in a limited resources environment. Therefore, VGGNet-16 and MobileNet-V2 were selected as the classification models. The experimental results show that the model could learn from the preprocessed images and obtain satisfactory classification results from pre-trained VGGNet-16 with the highest accuracy and F1-score of 95.00% and 95.23%, respectively. MobileNet-V2 inference time was approximately 10 times faster but the performance was lower with the highest accuracy and F1-score of 93.00% and 93.32%, respectively. © 2022 IEEE.

关键词： Image classification

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Integration and Optimization of EV Charging Processes in a Decentralized Local Energy Trading Market

Integration and Optimization of EV Charging Processes in a D...

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Omni-layer intelligent systems (COINS), International Conference on

作者： Christoph Groβ Tin Stribor Sohn Oliver Bringmann FZI Research Center for Information Technology Karlsruhe Germany Department of Computer Science Faculty of Science Embedded Systems Group University of Tübingen

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

This paper explores the integration of electric mobility into local energy trading markets and the challenges posed by the energy demand of Electric Vehicles (EVs) to the autonomy of these decentralized systems. An innovative approach is proposed, which involves EV charging station interfaces, smart contracts, and an optimization mechanism to balance participants' mobility needs with energy autonomy. The presented simulation demonstrates that the proposed approach improves autonomy and local energy procurement by 30% when compared to non-optimized charging. This offers a sustainable solution for energy trading in the context of increasing electric mobility.

关键词： Procurement Smart contracts Charging stations Electric vehicle charging Power grids intelligent systems Optimization

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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Analysis and Measurement of Electrically Small Surface Mount Inductor as Radiating Element for WBAN Communication in MedRadio Band

Analysis and Measurement of Electrically Small Surface Mount...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Harikrishna Kambham Meera Ramprasad Abhishek Srivastava Center for VLSI and Embedded Systems Technology (CVEST) IIIT Hyderabad India Birla Institute of Technology and Science Pilani Goa India

Wireless body area network (WBAN) communications in 400 MHz Medical Device Radio-communication (MedRadio) band seek wearable and implantable sensor nodes with extremely small antennas for reduced form factor. However, it is very challenging to shrink the 400 MHz antenna dimensions to sub-cm region for the conventional 50 Ω transmitters (TX) due to large wavelength. Thus there is a strong need to investigate extremely small, unconventional radiating elements with alternate TX topologies. In this work, we utilize extremely small surface mount inductors (SML) as a radiating element in non-50 Ω MedRadio band TX and present detailed fundamental analysis and models for its radiating-zone boundaries and corresponding path losses. To validate the proposed theory, simulation and measurement results of path loss of a cylindrical SML (1.2mm× 8.5mm) have been also presented, which show congruence with analytical models within ≤3 dB.

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