Sensor Readouts Research Articles

High-speed odor sensing using miniaturized electronic nose.

Animals have evolved to rapidly detect and recognize brief and intermittent encounters with odor packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results-existing solutions are either slow; or bulky, expensive, and power-intensive-limiting applicability in real-world scenarios for mobile robotics. Here, we introduce a miniaturized high-speed electronic nose, characterized by high-bandwidth sensor readouts, tightly controlled sensing parameters, and powerful algorithms. The system is evaluated on a high-fidelity odor delivery benchmark. We showcase successful classification of tens-of-millisecond odor pulses and demonstrate temporal pattern encoding of stimuli switching with up to 60 hertz. Those timescales are unprecedented in miniaturized low-power settings and demonstrably exceed the performance observed in mice. It is now possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.

Quantifying mechanical forces during vertebrate morphogenesis

Morphogenesis requires embryonic cells to generate forces and perform mechanical work to shape their tissues. Incorrect functioning of these force fields can lead to congenital malformations. Understanding these dynamic processes requires the quantification and profiling of three-dimensional mechanics during evolving vertebrate morphogenesis. Here we describe elastic spring-like force sensors with micrometre-level resolution, fabricated by intravital three-dimensional bioprinting directly in the closing neural tubes of growing chicken embryos. Integration of calibrated sensor read-outs with computational mechanical modelling allows direct quantification of the forces and work performed by the embryonic tissues. As they displace towards the embryonic midline, the two halves of the closing neural tube reach a compression of over a hundred nano-newtons during neural fold apposition. Pharmacological inhibition of Rho-associated kinase to decrease the pro-closure force shows the existence of active anti-closure forces, which progressively widen the neural tube and must be overcome to achieve neural tube closure. Overall, our approach and findings highlight the intricate interplay between mechanical forces and tissue morphogenesis.

Evaluating the Agreement between Oral, Armpit, and Ear Temperature Readings during Physical Activities in an Outdoor Setting.

Accurate body temperature measurement is essential for monitoring and managing safety during outdoor activities. Physical activities are an essential consideration for public health, with sports taking up an important proportion of these. Athletes' performances can be directly affected by body temperature fluctuations, with overheating or hypothermia posing serious health risks. Monitoring these temperatures allows coaches and medical staff to make decisions that enhance performance and safety. Traditional methods, like oral, axillary, and tympanic readings, are widely used, but face challenges during intense physical activities in real-world environments. This study evaluated the agreement, correlation, and interchangeability of oral, axillary, and tympanic temperature measurements in outdoor exercise conditions. Systems developed for specific placements might generate different sensor readouts. Conducted as an observational field study, it involved 21 adult participants (11 males and 10 females, average age 25.14 ± 5.80 years) that underwent the Yo-Yo intermittent recovery test protocol on an outdoor court. The main outcomes measured were the agreement and correlation between temperature readings from the three methods, both before and after exercise. The results indicate poor agreement between the measurement sites, with significant deviations observed post-exercise. Although the Spearman correlation coefficients showed consistent temperature changes post-exercise across all methods, the standard deviations in the pairwise comparisons exceeded 0.67 °C. This study concluded that widely used temperature measurement methods are challenging to use during outdoor exercises and should not be considered interchangeable. This variability, especially after exercise, underscores the need for further research using gold standard temperature measurement methods to determine the most suitable site for accurate readings. Care should thus be taken when temperature screening is done at scale using traditional methods, as each measurement site should be considered within its own right.

Low-Coherence Homodyne Interferometer for Sub-Megahertz Fiber Optic Sensor Readout.

This study proposes a method for interferometric fiber optic sensor readouts. The method utilizes the advantages of the active homodyne demodulation technique and low-coherence interferometry. The usage of the tandem low-coherence interferometer enables modulating the reference interferometer without any changes to the sensor. This achieves high sensitivity, high stability, and a wide frequency band. A sensitivity of up to 0.1 nm (RMS) in the frequency range of 5 kHz is demonstrated by detecting acoustic signals with a fiber Michelson interferometer as a sensor.

Integrating cloud and mist computing to lower latency in IoT topologies

AbstractTraditional IoT topologies involve access and core networks that share a common edge. On this edge, border routers and gateways are responsible for converting protocols at different layers of the stack. Devices like sensors and actuators sit on the access network while applications are located on the core network. The application performs predictions that trigger actuation based on received sensor readouts. Prediction, in turn, is the result of machine learning (ML) algorithms that are typically executed on the cloud. An alternative to this approach consists of performing the prediction on constrained devices on the IoT access network. This leads to Tiny ML (TinyML) and mist computing. In this context, there is a trade‐off between latency and computational power that becomes a deciding factor when choosing the application to carry on predictions. This paper introduces an algorithm that can be used to dynamically select the right application based on network layer parameters.

Locality sensitive hashing via mechanical behavior

From healing wounds to maintaining homeostasis in cyclically loaded tissue, living systems have a phenomenal ability to sense, store, and respond to mechanical stimuli. Broadly speaking, there is significant interest in designing engineered systems to recapitulate this incredible functionality. In engineered systems, we have seen significant recent computationally driven advances in sensing and control. And, there has been a growing interest – inspired in part by the incredible distributed and emergent functionality observed in the natural world – in exploring the ability of engineered systems to perform computation through mechanisms that are fundamentally driven by physical laws. In this work, we focus on a small segment of this broad and evolving field: locality sensitive hashing via mechanical behavior. Specifically, we will address the question: can mechanical information (i.e., loads) be transformed by mechanical systems (i.e., converted into sensor readouts) such that the mechanical system meets the requirements for a locality sensitive hash function? Overall, we not only find that mechanical systems are able to perform this function, but also that different mechanical systems vary widely in their efficacy at this task. Looking forward, we view this work as a starting point for significant future investigation into the design and optimization of mechanical systems for conveying mechanical information for downstream computing.

REST and EDA architectures in IoT actuation

While most Internet of Things (IoT) solutions involve sensing, some of them also introduce actuation mechanisms. Specifically, devices interact with assets in the environment and transmit sensor readouts to applications that perform analytics. These applications typically reside on the network core and, in turn, process the readouts that trigger the transmission of actuation commands to the device. One important issue in these schemes is the nature of the communication channels. Most devices are wireless and therefore they are affected by the effects of signal multipath fading that results in application loss. More importantly, these impairments may cause actuation commands to be lost or to be critically delayed. In this context, several standard mechanisms have been proposed for the transmission of traffic from the application to the devices. They fall under two main architectural categories: (1) Representational State Transfer (REST) and (2) Event Driven Architecture (EDA). In this paper, we analyze two protocols associated with each of these two architectures by comparing them in order to assess their efficiency in IoT actuation solutions. This analysis leads to the development of a mathematical model that drives an algorithm that enables the dynamic selection of the right technology based on network impairments.

Calibration of Soil Moisture Sensors (ECH2O-5TE) in Hot and Saline Soils with New Empirical Equation

The use of soil moisture sensors is a practice applied to improve irrigation water management. ECH2O-5TE sensors are increasingly being used to estimate the volumetric water content (VWC). In view of the importance of the efficient use of these devices, six main factors affecting the accuracy of sensor measurements were studied: soil moisture levels, soil salinity, temperature, organic matter, soil texture, and bulk density. The study showed that the electrical conductivity of the soil and the temperature independently affect the measurements, while the influence of other factors interferes with that of salinity. This study found that the sensor measurements of the VWC were closest to the actual VWC at the soil ECe and temperatures of 2.42 dS m−1 and 25 °C, with root-mean-square errors (RMSE) of 0.003 and 0.004 m3 m−3. Otherwise, the measured VWC values of these sensor readouts significantly overestimated the actual VWC, with an increasing soil ECe and/or producing temperatures higher than the stated values, and vice versa. Given the importance of these sensors for obtaining accurate measurements for water management, a simplified empirical equation was derived using the data collected from a wide range of measurements to correct the influences of electrical conductivity and temperature on the measurement accuracy of the sensors, while considering the influence of the soil’s texture. Thus, the following equation was proposed: ϴva = θvsaECe2+bECe+c+dT2+eT+f−1. The results concerning the measurement of different VWC levels via these sensors and the proposed L&O correction equation were compared with the corresponding actual VWC values determined by gravimetric methods. It was found that this empirical equation reduced the differences in the RMSE between the sensor readings for the VWC and the actual VWC from 0.072 and 0.252 to 0.030 and 0.030 m3 m−3 for 1 and 5 dS m−1, respectively, with respect to the EC’s influence at 25 °C and reduced the RMSE from 0.053 and 0.098 to 0.007 and 0.011 at 3 and 50 °C, respectively, regarding the effect of the temperature at EC 2.42 dS m−1 at different levels of the actual VWC values.

Integrating mechanical sensor readouts into organ-on-a-chip platforms.

Organs-on-a-chip have emerged as next-generation tissue engineered models to accurately capture realistic human tissue behaviour, thereby addressing many of the challenges associated with using animal models in research. Mechanical features of the culture environment have emerged as being critically important in designing organs-on-a-chip, as they play important roles in both stimulating realistic tissue formation and function, as well as capturing integrative elements of homeostasis, tissue function, and tissue degeneration in response to external insult and injury. Despite the demonstrated impact of incorporating mechanical cues in these models, strategies to measure these mechanical tissue features in microfluidically-compatible formats directly on-chip are relatively limited. In this review, we first describe general microfluidically-compatible Organs-on-a-chip sensing strategies, and categorize these advances based on the specific advantages of incorporating them on-chip. We then consider foundational and recent advances in mechanical analysis techniques spanning cellular to tissue length scales; and discuss their integration into Organs-on-a-chips for more effective drug screening, disease modeling, and characterization of biological dynamics.

A 96.9-dB-Resolution 109-μW Second-Order Robust Closed-Loop VCO-Based Sensor Interface for Multiplexed Single-Ended Resistance Readout in 180-nm CMOS

This article presents a highly digital robust voltage-controlled oscillator (VCO)-based front end for multiplexed single-ended resistive sensor readout applications. The architecture features a modified digital phase-locked loop (DPLL) structure that enables second-order noise shaping without any operational transconductance amplifiers (OTAs) and a single feedback digital-to-analog converter (DAC). The direct conversion of the sensor input resistance to time-domain information obviates the need for any conditioning circuit, thus resulting in a highly digital and area-compact architecture. The closed loop substantially reduces the amplitude of the signal at the VCO input, thereby achieving high linearity. To ensure high robustness against supply and temperature variations, a double measurement approach is employed. Fabricated in a 180-nm CMOS process with a 0.1-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> active area, the sensor readout consumes 109 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> of power and achieves a resolution of 96.9 dB for 0.5-ms conversion time, resulting in a Schreier figure of merit (FoM) of 166.5 dB. The circuit only requires a cheap and efficient single-point trimming calibration at room temperature. The low conversion time enables multiplexing among multiple sensor readouts. The supply and temperature sensitivities are 0.4%/V and 36 ppm/°C, respectively.

An integrated wearable microneedle array for the continuous monitoring of multiple biomarkers in interstitial fluid.

Implementations of wearable microneedle-based arrays of sensors for the monitoring of multiple biomarkers in interstitial fluid have lacked system integration and evidence of robust analytical performance. Here we report the development and testing of a fully integrated wearable array of microneedles for the wireless and continuous real-time sensing of two metabolites (lactate and glucose, or alcohol and glucose) in the interstitial fluid of volunteers performing common daily activities. The device works with a custom smartphone app for data capture and visualization, comprises reusable electronics and a disposable microneedle array, and is optimized for system integration, cost-effective fabrication via advanced micromachining, easier assembly, biocompatibility, pain-free skin penetration and enhanced sensitivity. Single-analyte and dual-analyte measurements correlated well with the corresponding gold-standard measurements in blood or breath. Further validation of the technology in large populations with concurrent validation of sensor readouts through centralized laboratory tests should determine the robustness and utility of real-time simultaneous monitoring of several biomarkers in interstitial fluid.

Rapid detection of microorganisms in a fish infection microfluidics platform

Inadequate access to clean water is detrimental to human health and aquatic industries. Waterborne pathogens can survive prolonged periods in aquatic bodies, infect commercially important seafood, and resist water disinfection, resulting in human infections. Environmental agencies and research laboratories require a relevant, portable, and cost-effective platform to monitor microbial pathogens and assess their risk of infection on a large scale. Advances in microfluidics enable better control and higher precision than traditional culture-based pathogen monitoring approaches. We demonstrated a rapid, high-throughput fish-based teleost (fish)-microbe (TelM) microfluidic-based device that simultaneously monitors waterborne pathogens in contaminated waters and assesses their infection potential under well-defined settings. A chamber-associated port allows direct access to the animal, while the transparency of the TelM platform enables clear observation of sensor readouts. As proof-of-concept, we established a wound infection model using Pseudomonas aeruginosa-contaminated water in the TelM platform, where bacteria formed biofilms on the wound and secreted a biofilm metabolite, pyoverdine. Pyoverdine was used as fluorescent sensor to correlate P. aeruginosa contamination to infection. The TelM platform was validated with environmental waterborne microbes from marine samples. Overall, the TelM platform can be readily applied to assess microbial and chemical risk in aquatic bodies in resource-constrained settings.

Processing RGB Color Sensors for Measuring the Circadian Stimulus of Artificial and Daylight Light Sources

The three main tasks of modern lighting design are to support the visual performance, satisfy color emotion (color quality), and promote positive non-visual outcomes. In view of large-scale applications, the use of simple and inexpensive RGB color sensors to monitor related visual and non-visual illumination parameters seems to be of great promise for the future development of human-centered lighting control systems. In this context, the present work proposes a new methodology to assess the circadian effectiveness of the prevalent lighting conditions for daylight and artificial light sources in terms of the physiologically relevant circadian stimulus (CS) metric using such color sensors. In the case of daylight, the raw sensor readouts were processed in such a way that the CIE daylight model can be applied as an intermediate step to estimate its spectral composition, from which CS can eventually be calculated straightforwardly. Maximal CS prediction errors of less than 0.0025 were observed when tested on real data. For artificial light sources, on the other hand, the CS approximation method of Truong et al. was applied to estimate its circadian effectiveness from the sensor readouts. In this case, a maximal CS prediction error of 0.028 must be reported, which is considerably larger compared to daylight, but still in an acceptable range for typical indoor lighting applications. The use of RGB color sensors is thus shown to be suitable for estimating the circadian effectiveness of both types of illumination with sufficient accuracy for practical applications.

Secure and Trustworthy NFC-Based Sensor Readout for Battery Packs in Battery Management Systems

Wireless battery management systems (BMSs) are increasingly being considered for modern applications. The ever-increasing complexity and production costs of BMS modules and wired connections resulted in a necessity for new ideas and approaches. Despite this growing trend, there is a lack of generic solutions focused on battery cells’ sensor readout, where wireless communication allows for a more flexible and cost-efficient sensor installation in battery packs. Many wireless technologies, such as those that use the 2.4GHz frequency band, suffer from interference and other limitations. In this article, we present an alternative approach to communication in BMS that relies on the use of near field communication (NFC) technology for battery sensor readouts. As an answer to the rising concern over the counterfeited battery packs, we consider an authentication schema for battery pack validation. We further consider security measures for the processed and stored BMS status data. To show that a general BMS application can make use of our design, we implement a BMS demonstrator using the targeted components. We further test the demonstrator on the technical and functional level, by also performing evaluation on its performance, energy usage, and a security threat model.

Implementation of automatic manual transmission for a two wheeler

The concept of Automatic Manual Transmission (AMT) is picking up speed in India after the launch of the Maruti Suzuki Celerio. But very few of the two-wheeler manufacturers in the world have tried to implement the AMT concept in a motorcycle. There are gearless motorcycles available which use a Continuously Variable Transmission (CVT) and a Centrifugal clutch, but this setup is very inefficient compared to a positive gear drivetrain. Thus these gearless motorcycles get low mileage and are also more expensive to manufacture. Geared motorcycles, on the other hand, have better starting torque, greater mileage and a higher span of gear ratios. They are also cheaper, but the average buyer is discouraged by the complexity of the operation. Even those familiar with riding a motorcycle do not shift optimally to obtain the best mileage/performance. The aim of this paper is to present an innovative AMT system developed and implemented on a manual geared motorcycle having a sequential constant-mesh gearbox with a multi-plate clutch. This was carried out by interfacing the manual gear shifting motorcycle with a microcontroller and using an electronic actuation mechanism to make it an automatic gear shifting motorcycle. The significant steps of this project would involve the sensor readouts of the parameters necessary, the actuation of the controls and, most importantly, the algorithm for the optimal shift points for a given motorcycle. These would be obtained experimentally and using the manufacturer data, if available. This AMT setup has been successfully developed and implemented. This setup can then be applied to any geared motorcycle with minimal structural changes without any modification to its existing gearbox. This converts the manual geared motorcycle into the automatic gear shifting motorcycle having better fuel economy and lower maintenance costs as compared to the conventional CVT motorcycles.

Modeling the Impact on Wheel Sensor Readouts by Eddy Current Brakes Installed in High-Speed Trains

This paper presents a model to anticipate the impact of Eddy Current Brakes (ECBs) installed in high-speed trains on the readouts of rail-side wheel sensors. The purpose is to anticipate false positive readouts of train wheels when traversing, one of the main obstacles for full ECB deployment. The ECB type EWB 154 from Knorr-Bremse and Wheel Sensor types RSR180 and RSR123 from Frauscher Sensor Technology are represented in a comprehensive model, integrating LTSpice and CST Microwave Studio. The wheel sensor predicted readout error is 4% compared to measurements when DC current is not applied to the ECB (passive case). It is demonstrated that the RSR180 is not compatible with ECBs, whereas the RSR123 is. The impact of active (DC current fed) brakes is analyzed when performing running tests with a high-speed ICE 3 train equipped with ECBs. The model is adjusted to study the saturation of the rail and ECB pole cores. The extra damping of the wheel sensor fingerprint is modeled by an extra 6% drop that may well be applicable to passive tests in a laboratory setting to shift to active tests without actually performing them. In this way, cost and time would be saved. Based on the model outcomes, a test bench is recommended for laboratory tests to emulate active behavior.

Lanthanide-Based Nanosensors: Refining Nanoparticle Responsiveness for Single Particle Imaging of Stimuli.

Lanthanide nanoparticles (LNPs) are promising sensors of chemical, mechanical, and temperature changes; they combine the narrow-spectral emission and long-lived excited states of individual lanthanide ions with the high spatial resolution and controlled energy transfer of nanocrystalline architectures. Despite considerable progress in optimizing LNP brightness and responsiveness for dynamic sensing, detection of stimuli with a spatial resolution approaching that of individual nanoparticles remains an outstanding challenge. Here, we highlight the existing capabilities and outstanding challenges of LNP sensors, en-route to nanometer-scale, single particle sensor resolution. First, we summarize LNP sensor read-outs, including changes in emission wavelength, lifetime, intensity, and spectral ratiometric values that arise from modified energy transfer networks within nanoparticles. Then, we describe the origins of LNP sensor imprecision, including sensitivity to competing conditions, interparticle heterogeneities, such as the concentration and distribution of dopant ions, and measurement noise. Motivated by these sources of signal variance, we describe synthesis characterization feedback loops to inform and improve sensor precision, and introduce noise-equivalent sensitivity as a figure of merit of LNP sensors. Finally, we project the magnitudes of chemical and pressure stimulus resolution achievable with single LNPs at nanoscale resolution. Our perspective provides a roadmap for translating ensemble LNP sensing capabilities to the single particle level, enabling nanometer-scale sensing in biology, medicine, and sustainability.

A 119dB Dynamic Range Charge Counting Light-to-Digital Converter For Wearable PPG/NIRS Monitoring Applications.

This paper presents a low power, high dynamic range (DR), reconfigurable light-to-digital converter (LDC) for photoplethysmogram (PPG), and near-infrared spectroscopy (NIRS) sensor readouts. The proposed LDC utilizes a current integration and a charge counting operation to directly convert the photocurrent to a digital code, reducing the noise contributors in the system. This LDC consists of a latched comparator, a low-noise current reference, a counter, and a multi-function integrator, which is used in both signal amplification and charge counting based data quantization. Furthermore, a current DAC is used to further increase the DR by canceling the baseline current. The LDC together with LED drivers and auxiliary digital circuitry are implemented in a standard 0.18μm CMOS process and characterized experimentally. The LDC and LED drivers consume a total power of 196μW while achieving a maximum 119dB DR. The charge counting clock, and the pulse repetition frequency of the LED driver can be reconfigured, providing a wide range of power-resolution trade-off. At a minimum power consumption of 87μW, the LDC still achieves 95dB DR. The LDC is also validated with on-body PPG and NIRS measurement by using a photodiode (PD) and a silicon photomultiplier (SIPM), respectively.

New Analysis Suggests There Is No Such Thing as Harmless Head Contact in Football

New Analysis Suggests There Is No Such Thing as Harmless Head Contact in Football

1.2-V Energy-Efficient Wireless CMOS Potentiostat for Amperometric Measurements

Wireless biosensors are playing a pivotal role in health monitoring, disease detection and management. The development of wireless biosensor nodes and networks strongly relies on the design of novel low-power, low-cost and flexible CMOS sensor readouts. This brief presents a CMOS potentiostat that integrates a control amplifier, a dual-slope ADC and a wireless unit on the same chip. It implements a novel time-based readout scheme, whereby the counter of the dual-slope ADC is moved to the receiver and the sensor current is encoded in the timing between two wireless pulses transmitted via pulse-harmonic modulation across an inductive link. Measured results show that the potentiostat chip can resolve a minimum input current of 10pA at a sampling frequency of 125 Hz and a power consumption of $12~\mu \text{W}$ .