Pub Date : 2022-06-12DOI: 10.1109/prime55000.2022.9816796
P. Piedimonte, L. Sola, M. Chiari, G. Ferrari, M. Sampietro
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.
{"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}
Pub Date : 2022-06-12DOI: 10.1109/prime55000.2022.9816766
Philipp Löhler, Andreas Pickhinke, Andreas Erbslöh, R. Kokozinski, K. Seidl
For retinal prostheses strategies to increase the stimulative cell selectivity are required to generate neural responses to electrical stimulation of retinal ganglion cells (RGCs) that match the response of the natural signal pathway. An important part of these strategies is the modulation of stimulus amplitude and frequency in the kilohertz range. The aim of this research is to investigate the electronic challenges and requirements of new electrical stimulation strategies for future retinal implants. This paper presents a 42 channel current controlled stimulator which is able to stimulate retinal tissue with sinusoidal frequencies higher than 1 kHz at amplitudes of up to 200 $mu {mathrm A}$. The power efficiency of the stimulator is 87.3% at a supply voltage of 1.8 V. One stimulator requires a respective area of 0.0071 $mathrm{mm}^{2}$ by using a 180 nm CMOS technology.
{"title":"SoC for Retinal Ganglion Cell Stimulation with Integrated Sinusoidal Kilohertz Frequency Waveform Generation","authors":"Philipp Löhler, Andreas Pickhinke, Andreas Erbslöh, R. Kokozinski, K. Seidl","doi":"10.1109/prime55000.2022.9816766","DOIUrl":"https://doi.org/10.1109/prime55000.2022.9816766","url":null,"abstract":"For retinal prostheses strategies to increase the stimulative cell selectivity are required to generate neural responses to electrical stimulation of retinal ganglion cells (RGCs) that match the response of the natural signal pathway. An important part of these strategies is the modulation of stimulus amplitude and frequency in the kilohertz range. The aim of this research is to investigate the electronic challenges and requirements of new electrical stimulation strategies for future retinal implants. This paper presents a 42 channel current controlled stimulator which is able to stimulate retinal tissue with sinusoidal frequencies higher than 1 kHz at amplitudes of up to 200 $mu {mathrm A}$. The power efficiency of the stimulator is 87.3% at a supply voltage of 1.8 V. One stimulator requires a respective area of 0.0071 $mathrm{mm}^{2}$ by using a 180 nm CMOS technology.","PeriodicalId":142196,"journal":{"name":"2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)","volume":"28 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":"125981714","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}
Pub Date : 2022-06-07DOI: 10.48550/arXiv.2206.07652
Francesco Daghero, D. J. Pagliari, M. Poncino
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.
{"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}
Pub Date : 2022-04-22DOI: 10.48550/arXiv.2204.10539
Chen Xie, D. J. Pagliari, A. Calimera
Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a $8times 8$ low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy).
{"title":"Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference","authors":"Chen Xie, D. J. Pagliari, A. Calimera","doi":"10.48550/arXiv.2204.10539","DOIUrl":"https://doi.org/10.48550/arXiv.2204.10539","url":null,"abstract":"Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a $8times 8$ low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy).","PeriodicalId":142196,"journal":{"name":"2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124714358","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}
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