Design and Modeling of a Low-Power Multi-Channel Integrated Circuit for Infrared Gas Recognition

Peer reviewe

A 70umx70um CMOS Digital Active Pixel Sensor for Digital Mammography and X-ray Imaging

Peer reviewe

Low-Power and Compact CMOS APS Circuits for Hybrid Cryogenic Infrared Fast Imaging

Peer reviewe

A 0.3mW/Ch 1.25V Piezo-Resistance Digital ROIC for Liquid Dispensing MEMS

Peer reviewe

A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility in Barcelona

As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the "15-minutes city" have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relationship. Here, we use mobile phone, census, and volunteered geographical data to measure geographic variations in the relationship between origin-destination flows and local urban accessibility in Barcelona. Using a Negative Binomial Geographically Weighted Regression model, we show that, globally, people tend to visit neighborhoods with better access to education and retail. Locally, these and other features change in sign and magnitude through the different neighborhoods of the city in ways that are not explained by administrative boundaries, and that provide deeper insights regarding urban characteristics such as rental prices. In conclusion, our work suggests that the qualities of a 15-minutes city can be measured at scale, delivering actionable insights on the polycentric structure of cities, and how people use and access this structure.Comment: 32 pages, 7 figure

A 1024-Channel 10-Bit 36-μW/ch CMOS ROIC for Multiplexed GFET-Only Sensor Arrays in Brain Mapping

This paper presents a 1024-channel neural read-out integrated circuit (ROIC) for solution-gated GFET sensing probes in massive muECoG brain mapping. The proposed time-domain multiplexing of GFET-only arrays enables low-cost and scalable hybrid headstages. Low-power CMOS circuits are presented for the GFET analog frontend, including a CDS mechanism to improve preamplifier noise figures and 10-bit 10-kS/s A/D conversion. The 1024-channel ROIC has been fabricated in a standard 1.8-V 0.18-mum CMOS technology with 0.012 mm 2 and 36 mu W per channel. An automated methodology for the in-situ calibration of each GFET sensor is also proposed. Experimental ROIC tests are reported using a custom FPGA-based muECoG headstage with 16times 32 and 32times 32 GFET probes in saline solution and agar substrate. Compared to state-of-art neural ROICs, this work achieves the largest scalability in hybrid platforms and it allows the recording of infra-slow neural signals

A Compact and Low-Power CMOS Circuit for Fully-Integrated NEMS Resonators

Peer reviewe

Compact CMOS Current Conveyor for Integrated NEMS Resonators

Peer reviewe

Real-time smart multisensing wearable platform for monitoring sweat biomarkers during exercise

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise in hot and humid conditions. Real-time noninvasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. In this work, we describe a platform that in- cludes different sweat biomonitoring prototypes of cost-effective, smart wearable devices for continuous biomonitoring of sweat during exercise. One prototype is based on conformable and disposable soft sensing patches with an integrated multi-sensor array requiring the integration of different sensors and printed sensors with their corresponding functionalization protocols on the same substrate. The second is based on silicon based sensors and paper microfluidics. Both platforms integrate a multi-sensor array for measuring sodium, potassium, and pH in sweat. We show preliminary results obtained from the multi-sensor prototypes placed on two athletes during exercise. We also show that the machine learning algorithms can predict the percentage of body weight loss during exercise from biomarkers such as heart rate and sweat sodium concentration collected over multiple subjects

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. This work describes a wearable sweat biomonitoring patch incorporating printed electrochemical sensors into a plastic microfluidic sweat collector and data analysis that shows the real-time recorded sweat biomarkers can be used to predict a physiological biomarker. The system was placed on subjects carrying out an hour-long exercise session and results were compared to a wearable system using potentiometric robust silicon-based sensors and to commercially available HORIBA-LAQUAtwin devices. Both prototypes were applied to the real-time monitoring of sweat during cycling sessions and showed stable readings for around an hour. Analysis of the sweat biomarkers collected from the printed patch prototype shows that their real-time measurements correlate well (correlation coefficient ≥0.65 ) with other physiological biomarkers such as heart rate and regional sweat rate collected in the same session. We show for the first time, that the real-time sweat sodium and potassium concentration biomarker measurements from the printed sensors can be used to predict the core body temperature with root mean square error (RMSE) of 0.02 °C which is 71% lower compared to the use of only the physiological biomarkers. These results show that these wearable patch technologies are promising for real-time portable sweat monitoring analytical platforms, especially for athletes performing endurance exercise