2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)最新文献_第10页

Differential Impedance Biosensing platform for early diagnosis of viral infections 差分阻抗生物传感平台用于病毒感染的早期诊断

2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME) Pub Date : 2022-06-12 DOI: 10.1109/prime55000.2022.9816796

P. Piedimonte, L. Sola, M. Chiari, G. Ferrari, M. Sampietro

{"title":"Differential Impedance Biosensing platform for early diagnosis of viral infections","authors":"P. Piedimonte, L. Sola, M. Chiari, G. Ferrari, M. Sampietro","doi":"10.1109/prime55000.2022.9816796","DOIUrl":"https://doi.org/10.1109/prime55000.2022.9816796","url":null,"abstract":"Detection of viruses is essential for the control and prevention of viral infections. In recent years, there has been a focus on simpler and faster detection methods, particularly through the use of electronic-based detection in a point-of-care configuration. The proposed biosensor platform can provide high-resolution measurements of viral infections by detecting antibodies. The system is based on differential impedance measurement of the biological target with nanoparticle amplification. The surface of the sensor is biochemically functionalized with a synthetic peptide to mimic the antigenic determinant of the targeted virion particle. Gold interdigitated microelectrodes are the core of the biosensing system. They are designed in a differential configuration, reference and active sensor, to counteract all possible mismatches such as temperature fluctuations and variations in the ion content of the solution. The successful combination of these elements makes it possible to reach a limit of detection of the system below 100 pg/mL for IgG antibodies in buffer. Furthermore, the biosensing system has been challenged with infected human serum samples for digital counts of antidengue virus antibodies, achieving the detection of clinically relevant target concentrations.","PeriodicalId":142196,"journal":{"name":"2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127247114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SoC for Retinal Ganglion Cell Stimulation with Integrated Sinusoidal Kilohertz Frequency Waveform Generation 基于集成正弦千赫兹频率波形的视网膜神经节细胞刺激系统

2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME) Pub Date : 2022-06-12 DOI: 10.1109/prime55000.2022.9816766

Philipp Löhler, Andreas Pickhinke, Andreas Erbslöh, R. Kokozinski, K. Seidl

引用次数: 2

Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs 基于决策树和cnn的微控制器两阶段人体活动识别

2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME) Pub Date : 2022-06-07 DOI: 10.48550/arXiv.2206.07652

Francesco Daghero, D. J. Pagliari, M. Poncino

{"title":"Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs","authors":"Francesco Daghero, D. J. Pagliari, M. Poncino","doi":"10.48550/arXiv.2206.07652","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07652","url":null,"abstract":"Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classic Machine Learning (ML) models, whereas Deep Learning (DL), although reaching state-of-the-art accuracy, is less popular due to its high energy consumption, which poses a significant challenge for battery-operated and resource-constrained devices. In this work, we bridge the gap between on-device HAR and DL thanks to a hierarchical architecture composed of a decision tree (DT) and a one dimensional Convolutional Neural Network (ID CNN). The two classifiers operate in a cascaded fashion on two different sub-tasks: the DT classifies only the easiest activities, while the CNN deals with more complex ones. With experiments on a state-of-the-art dataset and targeting a single-core RISC-V MCU, we show that this approach allows to save up to 67.7% energy w.r.t. a “stand-alone” DL architecture at iso-accuracy. Additionally, the two-stage system either introduces a negligible memory overhead (up to 200 B) or on the contrary, reduces the total memory occupation.","PeriodicalId":142196,"journal":{"name":"2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130674575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference 基于低分辨率红外传感器和自适应推理的节能和隐私意识社交距离监测

2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME) Pub Date : 2022-04-22 DOI: 10.48550/arXiv.2204.10539

Chen Xie, D. J. Pagliari, A. Calimera

引用次数: 2