Small Sensor Research Articles

Device authentication with FPGA based self correcting Physical Unclonable Function for Internet of Things

The device authentication of IoT nodes is very important since they are mainly deployed in open unsecure environment. Physical Unclonable Function (PUF) is a fingerprint of chip and may be useful to solve the hardware security issues in IoT network. This fingerprint is based on the inevitable physical variations that occur during the manufacturing of the device and is useful for generating cryptographic keys. These variations are random in nature and extracted through Challenges–Response Pairs. However, the responses of the device are subjected to change in the environmental condition such as temperature variations, humidity alteration, aging effect and voltage variations. The paper presents reliable PUF solution using an efficient error correction scheme. The main contribution of the paper is to design a secure and effective self-error correctable PUF for tiny IoT nodes. The systematic Golay code and decoding algorithm are applied with PUF for improvement of the reliability in IoT applications. The hardware implementation of Golay code based error correction along with PUF is performed on FPGA board to validate the proof of concept as reliable hardware security solution. The proposed design has good performance with 45% uniqueness and 97% of reliability using efficient error correction mechanism. The hardware results show that our proposed self correcting PUF based solution would suited for small sensors in various IoT applications.

Implementation of a WSN for Environmental Monitoring: From the Base Station to the Small Sensor Node.

In this paper, the implementation of a Wireless Sensor Network (WSN) for environmental monitoring (EM) is presented. It includes the design, implementation and experimental characterization of a multi-sector base station (BS) antenna composed of several microstrip Quasi-Yagi elements and the implementation and experimental characterization of a reduced form factor antenna for the sensor nodes (SN). Subsequently, it reports the implementation of a WSN based on Lopy4 transceivers, using the developed BS and SN antennas. Finally, experimental results obtained on the field to evaluate the performance of the network in terms of maximum coverage distance and coverage area are presented. According to the field tests, the connectivity between the sensor nodes and the developed WSN base station is confirmed at distances above 3.5 km and for all the antenna sectors of the multi-sector BS attaining a 360 of field of view.

A point-to-point “cap” strategy to construct a highly selective dual-function molecularly-imprinted sensor for the simultaneous detection of HAV and HBV

As an artificial biomimetic receptor, molecularly-imprinted polymer (MIP) has been widely used for the separation, enrichment and detection of various substances. However, due to the complexity of virus structure, huge volume and the existence of highly similar viruses, MIP shows unsatisfactory selectivity in virus detection. To overcome these issues, two kinds of virus nanoMIPs, just like a “cap”, were synthesized by a solid-phase imprinting nanogel technique. The “cap” had no inner core and was much smaller than that of a conventional MIP, which was more favorable for mass transfer. Moreover, each “cap” could only combine with one target virus, which avoided the interference between large-volume virus molecules effectively. The two synthesized “caps” were mixed to construct a bifunctional MIP virus sensor for the simultaneous detection of Hepatitis A virus (HAV) and Hepatitis B virus (HBV). As expected, the selectivity factor (SF) for HBV detection reached 13.7, which was much higher than the reported virus MIP sensors (SF: 3–6), which was comparable to that of small molecular imprinting sensors. In addition, the high sensitivity toward HBV was 34.3 fM, and that of HAV was 27.1 pM. This method provides an idea for preparing high-selectivity biomacro-MIPs, as well as a method for the simultaneous detection of similar viruses with high sensitivity and selectivity. The recovery experiment of spiked serum showed that this method also has great practical application prospects.

(Digital Presentation) Size Effect on the Sensitivity of a Nano Temperature Sensor Based on Carbon Nanotubes

Nanosensors enable the development of small and portable devices capable of monitoring several parameters simultaneously. As such, carbon nanotubes (CNTs), which offer unique properties, play an essential role in realizing nanosensors. Since CNTs are sensitive to many parameters, the sensor's performance can be affected by the interaction of CNTs with surroundings. Accordingly, the size of the sensor is an important factor in determining its overall performance. In this study, we report the size effect on the temperature sensitivity of sensors based on single wall carbon nanotubes (SWCNTs) deposited on a sapphire substrate. Solution-processed fabrication method is utilized to fabricate temperature sensors. In this method, dielectrophoretic force is employed to trap SWCNTs in between gold nanoelectrodes patterned on a sapphire substrate using the standard photolithography method [1]. The size of the sensor is controlled by monitoring the conductance of the sensor, which is related to the number of SWCNTs trapped between gold nanoelectrodes during the trapping process. The results show that the smaller sensor provides higher sensitivity.

Quasi-Distributed Temperature Detection of Press-Pack IGBT Power Module Using FBG Sensing

In situ temperature near the chips in a multiple-chip power semiconductor module is important information for thermal design validation, condition monitoring, and overheat protection. Due to the compact packaging structure and clamping force, online detection of the temperature is challenging for a press-pack module. This study attempts to achieve measurement by integrating optical fiber Bragg grating (FBG) sensors into the device. Different integration schemes are examined using experiments and theoretical analysis to show the effects on the transient performance. It is shown that FBG sensors with plate housing can effectively indicate the transient temperature ripple in normal converter operation, owing to the fast dynamic response of a small size sensor. The package integration has only minimal effects on the original distributions of electrical, thermal, and mechanical quantities within the power module.

Design and Analysis of Small Size Eddy Current Displacement Sensor.

A systematic method is employed for the design and analysis of a small size eddy current (EC) displacement sensor. Simulations are first performed to determine the optimal winding structure and dimensions of the sensor. A linear-fitting approach is then developed for converting the AC displacement signal of the sensor to a DC signal. Finally, a compensation method is proposed for mitigating the temperature drift of the EC sensor under different working temperatures. The experimental results show that the proposed sensor has a sensitivity of approximately 3 μm, a working temperature range of 25–55 °C, and a linearity of ±1.025%.

Surface-potential-modulated piezoresistive effect of core–shell 3C-SiC nanowires

The effect of surface potential on the carrier mobility and piezoresistance of core–shell silicon carbide nanowires (SiC NWs) was investigated to realize small and sensitive SiC-microelectromechanical systems sensors. The p-type cubic crystalline SiC (3C-SiC) NWs were synthesized via the vapor–liquid–solid method and coated with silicon dioxide (SiO2) or aluminum oxide (Al2O3) dielectric shells to form core–shell structured NWs with different surface potentials. Four-point bending devices (FBDs) with a field-effect transistor (FET) configuration integrating a single core–shell 3C-SiC NW as the FET channel were fabricated to apply an additional electric field and strain to the core–shell 3C-SiC NWs. The fixed oxide charge densities of the SiO2 and Al2O3 shells showed positive and negative values, respectively, which were equivalent to electric fields of the order of several hundred thousand volt per centimeter in absolute values. In the core–shell 3C-SiC NWs with originally low impurity concentrations, the electric field induced by the fixed oxide charge of the shells can determine not only the electrical conduction but also the charge carriers in the NWs. Bending tests using the FBDs showed that the piezoresistive effect of the SiO2-coated NW was almost the same as that of the as-grown 3C-SiC NW reported previously, regardless of the gate voltage, whereas that of the Al2O3-coated NW was considerably enhanced at negative gate voltages. The enhancement of the piezoresistive effect was attributed to the piezo-pinch effect, which was more pronounced in the NW, where the carrier density at the core–shell interface is enhanced by the electric field of the dielectric.

Optimal architecture artificial neural network model design with exploitative alpha gray wolf optimization for soft calibration of CO concentration measurements in electronic nose applications

The low-cost and small size solid-state sensor arrays are suitable to implement a wide-area electronic nose (e-nose) for real-time air quality monitoring. However, accuracy of these low-cost sensors is not adequate for precise measurements of pollutant concentrations. Artificial neural network (ANN) estimation models are used for the soft calibration of low-cost sensor array measurements and significantly improve the accuracy of low-cost multi-sensor measurements. However, optimality of neural architecture affects the performance of ANN estimation models, and optimization of the ANN architecture for a training data set is essential to improve data-driven modeling performance of ANNs to reach optimal neural complexity and improved generalization. In this study, an optimal architecture ANN estimator design scheme is suggested to improve the estimation performance of ANN models for e-nose applications. To this end, a gray wolf optimization (GWO) algorithm is modified, and an exploitative alpha gray wolf optimization (EA-GWO) algorithm is suggested. This modification enhances local exploitation skill of the best alpha gray wolf search agent, and thus allows the fine-tuning of ANN architectures by minimizing a multi-objective cost function that implements mean error search policy. Experimental study demonstrates the effectiveness of optimal architecture ANN models to estimate CO concentration from the low-cost multi-sensor data.

Moving target localization in distributed MIMO radar systems with sensor position errors in the presence of a calibration object

This paper investigates the moving target localization problem in distributed multiple-input multiple-out (MIMO) radars with sensor position errors. Unlike the previous work, by invoking a moving calibration object, we develop a closed-form solution to determine the position and velocity of the unknown target for alleviating the performance degradation caused by sensor position errors in traditional methods. The proposed method has three stages. The calibration object is utilized in the first stage to estimate the sensor position errors and improve the sensor parameters, and these parameters are devoted to establishing the pseudo-linear equations by introducing nuisance variables in the second stage. After solving the pseudo-linear equations, the target parameters are refined by exploiting the relationships between the extra variables and target parameters in the final stage. The proposed method is shown analytically to reach the Cramér-Rao lower bound (CRLB) performance under sufficiently small measurement noise and sensor localization error conditions, and it can be easily extended to general cases where exist imprecise calibration object parameters or multiple calibration objects. Simulation results corroborate the theoretical analysis and show that the proposed method has a significant performance improvement over the state-of-the-art methods.

Volatile Olfactory Profiles of Umbrian Extra Virgin Olive Oils and Their Discrimination through MOX Chemical Sensors.

Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several scientific techniques were applied in order to guarantee the authenticity and quality of this EVOO. In the present study, the volatile compounds (VOCs) by gas chromatography–mass spectrometry with solid-phase micro-extraction detection (GC–MS SPME), organoleptic analysis by the official Slow Food panel and the detection by a Small Sensor System (S3) were applied. Ten EVOOs from Umbria, a central Italian region, were selected from the 2021 Slow Food Italian extra virgin olive oil official guide, which includes hundreds of high-quality olive oils. The results demonstrated the possibility to discriminate the ten EVOOs, even if they belong to the same Italian region, by all three techniques. The result of GC–MS SPME detection was comparable at the discrimination level to the organoleptic test with few exceptions, while the S3 was able to better separate some EVOOs, which were not discriminated perfectly by the other two methods. The correlation analysis performed among and between the three methodologies allowed us to identify 388 strong associations with a p value less than 0.05. This study has highlighted how much the mix of VOCs was different even among few and localized EVOOs. The correlation with the sensor detection, which is faster and chipper compared to the other two techniques, elucidated the similarities and discrepancies between the applied methods.

A MEMS Device Integrating Multiple Cantilever Displacement Sensors to Evaluate Flat Machined Surfaces

Multi-point scanning measurement, which is effective in eliminating motion errors of the stage in on-machine profile measurement, requires multiple displacement sensors of equal pitch to measure displacements simultaneously. However, it is not easy to arrange small sensors with high alignment accuracy when applying the multi-point method at a narrow pitch. In addition, if many sensors can be arranged in parallel, improvement in measurement accuracy can be expected. Therefore, a new micro electro mechanical system (MEMS) device for straightness measurement, one that integrates 10 cantilever displacement sensors, has been proposed. This device can be expected to solve the problem involved in the multi-point method because of the characteristics of MEMS, as the semiconductor processing method can make mechanical structures with high accuracy and it can easily make the device with many identical structures. The device is designed to measure waviness less than 100 μm in height. Ten cantilevers of 11 mm length are fabricated in parallel with 1.8 mm pitch on a side of a base substrate 20 mm square. The strain induced by a displacement of the probe placed near the front edge of the cantilever is detected as a change in the resistance of the piezo resistor at the foot of the cantilever. In the fabrication process of this device, crystal anisotropic etching is performed for 12 hours to form probes 250 μm high. A new fabrication process is also proposed in which a protective process is added to prevent damage to the circuits already formed during the etching. A prototype is investigated, and it is found that the resistance value increases about 0.45% in proportion to the displacement of 100 μm. It is therefore confirmed that this device has the basic ability to detect displacement.

Flex-PCB Quench Antenna Developments at FNAL

Flexible printed-circuit-board-based quench antenna arrays have been developed at FNAL since 2017. They proved to be low-noise, low-cost, sensitive, and non-invasive sensors for quench characterization in superconducting magnets, having the potential to extend even to quench detection. Starting with applications of small area sensors of this type, we have explored feasibility for operations at high pressure, and have invested in multi-channel quench antenna arrays covering the complete area of the innermost magnet layer of the conductor. We've also tested and optimized sensor geometry and relative orientations to increase sensitivity and spatial resolution. Beyond developments of warm and cold bore antennas, we've progressed to concepts which attach quench antennas to the conductor surfaces of accelerator magnets. Multiple programs and projects at FNAL and collaborating institutions benefited from this work already and our plans target further extensions of capabilities and applicability of these devices. In this paper we present the logic behind the development steps taken so far, status of our work and direction of near-term research on quench antennas at Fermilab.

Comparison of different sensor thicknesses and substrate materials for the monolithic small collection-electrode technology demonstrator CLICTD

Small collection-electrode monolithic CMOS sensors profit from a high signal-to-noise ratio and a small power consumption, but have a limited active sensor volume due to the fabrication process based on thin high-resistivity epitaxial layers. In this paper, the active sensor depth is investigated in the monolithic small collection-electrode technology demonstrator CLICTD. Charged particle beams are used to study the charge-collection properties and the performance of devices with different thicknesses both for perpendicular and inclined particle incidence. In CMOS sensors with a high-resistivity Czochralski substrate, the depth of the sensitive volume is found to increase by a factor two in comparison with standard epitaxial material and leads to significant improvements in the hit-detection efficiency and the spatial and time resolution.

A Survey on Various Congestion Control Techniques in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are made up of small battery-powered sensors that can sense and monitor a variety of environmental conditions. These devices are self-contained and fault tolerant. The majority of WSNs are built to perform data collection tasks. These data are gathered and then sent to the sink node. Small packets are sent towards the sink node in such cases, and as a result, the areas near the sink node become congested, becoming the bottleneck of the entire network. In this paper, a survey of existing techniques or methods for detecting and eliminating congestions is conducted. Finally, a comparison in the form of a table based on various matrices is presented.

P21. Home-based functional outcome measurements for spine patients: validation and feasibility study

<h3>BACKGROUND CONTEXT</h3> Functional outcome measurements (FOMs) have been shown to be effective tools in diagnosing, planning treatments and tracking outcomes in several subspecialties of medicine. FOMs often produce large volumes of complex data that require sophisticated collection, storage and analytic techniques to describe functional measures of items like gait performance, postural stability, neuromuscular activity and movement strategies. These complexities pose particular challenges for the application of FOMs in the clinical setting where the primary focus is on patient care. Currently, utilization of FOMs is primarily driven by individual surgeons explicitly interested in the development and dissemination of FOMs metrics and related technologies. Unfortunately, there are capital and support costs associated with running and maintaining FOMs labs which pose additional hurdles that may inhibit otherwise interested surgeons. As interest and use of clinically derived functional evaluations tailored specifically for spine patients grow, there is a need for simple, objective measures to summarize the complexity of modern motion tracking data sets to simple, clinically meaningful and interpretable terms. <h3>PURPOSE</h3> To develop and validate low-cost FOMs wearable sensor that quantifies common activities in a home-based environment. <h3>STUDY DESIGN/SETTING</h3> Prospective, concurrent control cohort study. <h3>PATIENT SAMPLE</h3> Twelve lumbar degenerative surgical candidates and 12 healthy controls participated in this study. <h3>OUTCOME MEASURES</h3> Level of activity, spatiotemporal parameters, and balance effort. <h3>METHODS</h3> Subjects wore a small noninvasive sensor (30 × 44 × 8mm, weight: 12 grams) with an adhesive patch on T1 in addition to traditional gait lab sensors. Validation of the proposed sensor and common FOMs metrics generated by a gait lab were compared. Furthermore, each subject wore the wearable sensor on T1 for additional 24 hours. The sensor detected different types and levels of activities during the day (ie, standing, walking, sitting, etc.) and also captured trunk kinematics in the home-based environment. <h3>RESULTS</h3> The wearable sensor was able to reliably measure all trunk kinematics during standing, walking and transition from sitting (p>0.05) when compared to gold standard human motion capture in a gait lab. The sensor data collected during the additional 24-hours at home was passed through a machine learning activity classification algorithm. The predicted results indicate that patients with degenerative lumbar spinal pathologies presented with a lower level of activity (walking: 4.7%, standing: 11.6%, sitting: 25.3%) in comparison to controls (walking: 7.9%, standing: 21.7%, sitting: 17.1%). Balance effort and the CoE dimensions were found to be significantly larger in these patients (sagittal: 7.9°, coronal: 7.2°) compared to controls (sagittal: 5.8°, coronal: 3.2°; p<0.035). <h3>CONCLUSIONS</h3> The purpose of this study was to validate and prove the feasibility of home-based functional outcome measurements, which can provide relevant details in a digestible format that conveys the functional status of the patient and raises flags for areas of concern. Such insights may lead to changes in assessments of disability, treatment strategies or modifications of rehabilitation regimens. Several benefits are anticipated from a wearable-based quantitative tool to assist with preoperative planning for patient-specific alignment objectives such as assisting in choosing the right surgical procedure for the right patient, recognition of red flags, leading to avoidance of surgery where it is not going to help, recovery monitoring, early detection of perioperative complications, prognostic information, and prediction of treatment outcomes. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

Real Time Monitoring of Wine Vinegar Supply Chain through MOX Sensors.

Vinegar is a fermented product that is appreciated world-wide. It can be obtained from different kinds of matrices. Specifically, it is a solution of acetic acid produced by a two stage fermentation process. The first is an alcoholic fermentation, where the sugars are converted in ethanol and lower metabolites by the yeast action, generally Saccharomyces cerevisiae. This was performed through a technique that is expanding more and more, the so-called “pied de cuve”. The second step is an acetic fermentation where acetic acid bacteria (AAB) action causes the conversion of ethanol into acetic acid. Overall, the aim of this research is to follow wine vinegar production step by step through the volatiloma analysis by metal oxide semiconductor MOX sensors developed by Nano Sensor Systems S.r.l. This work is based on wine vinegar monitored from the grape must to the formed vinegar. The monitoring lasted 4 months and the analyses were carried out with a new generation of Electronic Nose (EN) engineered by Nano Sensor Systems S.r.l., called Small Sensor Systems Plus (S3+), equipped with an array of six gas MOX sensors with different sensing layers each. In particular, real-time monitoring made it possible to follow and to differentiate each step of the vinegar production. The principal component analysis (PCA) method was the statistical multivariate analysis utilized to process the dataset obtained from the sensors. A closer look to PCA graphs affirms how the sensors were able to cluster the production steps in a chronologically correct manner.

Optimal design and implementation of tunnelling magnetoresistance based small current sensor with temperature compensation

This paper proposes a tunnelling magnetoresistance (TMR) based current sensor considering temperature error compensation, for the measurement of current in the range from a few hundred micro-amperes to a few hundred mini-amperes, in the applications in power systems. Theoretic and simulation analysis are studied for the optimization design of the TMR based small current sensor in the perspective of the magnetic loop structure, magnetic core parameters, and secondary circuit. An improved temperature error compensation circuit is proposed to suppress the sensitivity drift of the sensor. A prototype is built to verify the proposed design. According to the experiment results, the proposed TMR small current sensor has a measurement range of ±200 mA and a sensitivity of 10 V/A. The temperature error compensation scheme can reduce the temperature coefficient of sensitivity of the sensor from 1780 ppm/°C to 504 ppm/°C. The proposed TMR small current sensor can measure small current effectively.

Invariants in problems of pattern recognition and control of states of complex objects

Objective. The aim of the study is to build emulators of the main sensors of a small spacecraft (SC).Method. The quality of recovery is determined by the standard deviation (RMS) of the data from the standards. The study is based on the application of the dynamic programming method.Result. A review of works in the field of application of stable characteristics - invariants, in the applied areas of pattern recognition and state control of complex technical systems is carried out. A method aimed at identifying failures and building small spacecraft sensor emulators based on the analysis of stable indicators: correlation and covariance coefficients is proposed and tested. The proposed approach makes it possible to increase the overall reliability of the information-measuring system of a small spacecraft (SC). In the future, to emulate a failed sensor, it is planned to use multiple correlation of time sequences.Conclusion. In the general case, recognition is reduced to comparing the graph structures of objects to the correct matching of hierarchical image models based on various invariants. Control of the states of complex systems is based on the use of stable characteristics that act as invariant or subinvariant descriptors. Invariants in various applications help to recognize the current situation with a sufficient degree of accuracy, usually reducing the dimension of the problem.

Characteristic Analysis of a Chipless RFID Sensor Based on Multi-Parameter Sensing and an Intelligent Detection Method.

Chipless radio frequency identification (RFID) technology has been widely used in the field of structural health monitoring (SHM), but most of the current research mainly focuses on the detection of mechanical properties and there are few studies on the multi-physical parameters (for example, temperature and humidity) in the climatic environment around the structure. Thus, it is necessary to design a small and compact sensor for multi-parameter detection. This paper proposes a multi-parameter chipless RFID sensor based on microstrip coupling, which supports 4-bit ID code and integrates two detection functions of temperature and humidity. Through linear normalization fitting, the sensitivity of the sensor is about 2.18 MHz/RH in the ambient relative humidity test and the sensitivity of the sensor is about 898.63 KHz/°C in the experimental test of water bath heating from 24.6 °C to 75 °C. In addition, this paper proposes an engineering application detection method, designs a lightweight dynamic spectrum detection and wireless transmission platform based on a lightweight vector network analyzer (VNA) and realizes the real-time extraction and transmission of RFID spectrum sensing data. The means are more flexible and economical than traditional experimental scenarios.

Pen‐type pulse wave measurement system for detecting pathological changes in cardiac diseases

In this study, we have developed a new pen-type pulse wave measurement system to evaluate heart failure treatment based on cardiovascular information. In order to acquire minute pathological changes in diseases such as heart failure, the pen-type pulse wave measurement system is equipped with a high-resolution MEMS tactile sensor that achieves a displacement resolution of 0.4 μm and an input resolution of 50μN. This measurement system consists of a small sensor package, a pen-type measurement system, and a glass sensor package to enable physicians to measure the radial artery of men and women of all ages in a minimally invasive, rapid, and precise manner. Realizing this measurement system, simple and precise pulse wave measurement by direct measurement and measurement of pulse wave characteristics for individual differences has been possible.