Small Sensor Research Articles

Onorbit Operational Results of Small and High Resolution Optical Sensor (SHIROP)

Onorbit Operational Results of Small and High Resolution Optical Sensor (SHIROP)

Current Measurement Method of Multiple Chips Using Rectangular PCB Rogowski Coils Integrated in Press Pack IGBT Device

Unbalance of transient currents of multiple chips inside press-pack insulated-gate bipolar transistor (PP IGBT) results in degrading operation and even device damage. Chip currents measurement plays an essential role to understand and solve this problem. Printed-circuit board Rogowski coil (PCB RC), as a small, noninvasive, and high bandwidth current sensor, is very suitable for this application. However, PCB RC integrated with multiple Rogowski coils (RCs) is still difficult to be applied to measure the current of multiple chips in press pack IGBT device because of the low measurement accuracy due to the large interference. In this letter, a high accuracy PCB RC with ten rectangular RCs inside PP IGBT is proposed to measure ten chip currents at the same time. Different from single RC measuring single chip current, the interference of adjacent chip currents has to be considered, and is reduced to 0.6% by optimizing the turns arrangement and lead wires of RCs. The noise of cross talk of adjacent RCs is 1.7% by decreasing the capacitance coupling between RCs. Shielding layers and grounding patter of lead wires for ten RCs are proposed to eliminate the interference of collector voltage. The experiment of transient process of ten chips verifies the effectiveness of proposed PCB RC, and measurement error is only 1.8%.

A Multi-Hop QoS-Aware and Predicting Link Quality Estimation (PLQE) Routing Protocol for Reliable WBSN

Technological advancements and miniaturization of sensor have made it possible to use tiny size sensors nodes to monitor patients’ physiological data in real-time at very low cost. The network of such small size sensor nodes is often called wireless body sensor networks (WBSNs). The patient’s data is highly sensitive which required to reach reliably and timely at the destination node. However, short range wireless nodes and postural body movements cause frequent topological changes and recurrent network partitioning. The network partitioning leads to delayed and significant loss of critical patient’s data such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyogram (EMG). Therefore, QoS requirement of WBSNs in terms of reliability, end-to-end delay and throughput are highly compromise. We in this paper propose a Predicting routing protocol PLQE to address reliable data transmission and network partitioning due to node mobility. The PLQE dynamically determines the efficient links by using beta probability density function, link quality and link delay estimation (LDE). The PLQE is composed of link reliability factor (LRF) and expected probability Indicator (EPI) to obtain most often updated status of the links to figure out stable and reliable end to end route to the destination node. The simulation results confirm the higher performance of the proposed scheme against state of the art routing protocols in terms of packet delivery ratio, end to end delay, throughput and normalized routing load.

Fuzzy Logic Based Load Balanced Clustering for Network Lifetime Enhancement in WSN

Large number of small sensor nodes exists in WSN’s for sensing and collecting information from the environment. In today’s time, these sensor nodes were applied in under water, military area, health care, earthquake sensing and in dedicated areas with recent technologies. Sensor nodes have limited life time and have supplementary network life. Network lifecycle depends on many factors such as connectivity, residual energy, topology types, single hop, multi hop, distance from base station, distance to cluster heads and much more. Among the various solutions given, clustering is considered to be good solution and optimal cluster head selection leads to efficient energy consumption. This paper proposes fuzzy based multi-attributes clustering that balances load among sensor nodes and also gives energy efficient clustering. Here we have used some attributes such as delay, residual energy, distance to CH, standard deviation to average network lifetime and standard deviation to residual energy. Results and experimental analysis validates that the proposed methods outperforms other compared algorithms.

Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The growth in the use of small sensor devices, commonly known as the Internet of Things (IoT), has resulted in unprecedented amounts of data being generated and captured. With the rapidly growing popularity of personal IoT devices, the collection of personal data through such devices has also increased exponentially. To accommodate the anticipated growth in connected devices, researchers are now investigating futuristic network technologies that are capable of processing large volumes of information at much faster speeds. However, the introduction of innovative network technologies coupled with existing vulnerabilities of personal IoT devices and insufficient device security standards is resulting in new challenges for the security of data collected on these devices. While existing research has focused on the technical aspects of security vulnerabilities and solutions in either network or IoT technologies separately, this paper thoroughly investigates common aspects impacting IoT security on existing and futuristic networks, including human-centric issues and the mechanisms that can lead to loss of confidentiality. By undertaking a comprehensive literature review of existing research, this article has identified five key areas that impact IoT security for futuristic next generation networks. Furthermore, by extensively analysing each area, the article reports on conclusive findings and future research opportunities for IoT privacy and security for the next generation of network technologies</i> .

Cerebral Blood Flow Monitoring with Piezoeletric Film, Photoplethysmogram and an LSTM Neural Network

To improve monitoring of cerebral blood flow and arterial response to clinical interventions following acute traumatic brain injury, we have created a small sensor suite that can be mounted around the diameter of a typical intracranial catheter. This instrument is driven by the need to prevent subsequent ischemic injury after a traumatic brain injury, due to elevated intracranial pressure and compromised cerebral autoregulation. To minimize effects on clinician workflow, our sensors can be integrated into catheters that are currently being used in accordance with accepted standards of care. This paper describes the use of a combination of thin piezoelectric material, a photoplethysmogram, and a long short-term memory regression network to track cerebral blood flow fluctuations. The results show a correlation (R2 = 0.76) between beat-to-beat waveform features collected using our sensor suite and reference blood flow measurements by ultrasound imaging. This method we introduce may help give medical professionals timely data on patients’ cerebral hemodynamic status and how well patients responded to clinical interventions.

PrinType

We present PrinType, a fingerprint recognition based typing technique for virtual reality. Different regions of fingers covered by friction ridges are assigned to different keys (i.e. letters, numbers, punctuation, or functions). Once the thumb-worn fingerprint sensor touches a finger, the contact region (and its key) is recognized by matching the current image with registered templates. Using only a small sensor, PrinType turns each segment of all fingers into a touchable key. We designed keyboard layouts corresponding to three interaction sub-spaces: whole finger keyboard, fingertip keyboard, and single-handed keyboard. A 12-person user study was conducted to evaluate the performance of different strategies. Our user evaluation showed that participants achieved an average of 29.56, 32.38, and 34.22 WPM with 0.79%, 0.20%, and 0.21% not corrected error rate in the three strategies. In addition, we provided a detailed analysis of various micro metrics to further understand user performance and technical characteristics. Overall, PrinType is favored by users for its usability, efficiency, and novelty.

Shift invariant deep convolution neural learning for resource efficient healthcare data transmission in WSN

Wireless Sensor Network (WSN) comprises many small sensors nodes incorporated by IoT which plays vital role in several applications. IoT incorporates physical devices into a network structure that includes software, sensors to collect data from the surrounding environment, and sinks for data transmission. To increase network lifespan during data transmission, a resource-efficient routing system is needed. Different academics have been working on improving routing techniques recently to increase effectiveness and route discovery. The key issue affecting the performance of WSNs is an efficient resource usage of routing. In order to overcome this problem, a new technique called Hierarchical Point wise Mutual Informative Clustering-based Shift Invariant Deep Convolutive Neural Learning (HPMIC-SIDCNL) technique is introduced in WSN. The HPMIC-SIDCNL technique uses the Shift Invariant Convolutive Deep neural learning concept to learn the given input with help of several layers such as input, three hidden layers, and output layer. Initially, the IoT devices are used in sensor nodes for sensing and collecting patient data. In the input layer, sensor nodes are considered as input. Then input is transmitted into first hidden layer where resource of the sensor nodes such as energy and bandwidth are measured. Then estimated resources are transmitted into second hidden layer where the clustering process is carried out using the Hierarchical IBM Point wise Mutual Informative Clustering technique. The Hierarchical IBM clustering uses the Point wise Mutual Information for collection of sensor nodes depend on energy and bandwidth. In the third hidden layer, the cluster is selected on higher residual energy and minimum bandwidth for improving the data transmission and reduces the delay. The cluster head receives the patient data after it has been sent by the source node in that particular cluster. In order to perform data transmission from source to sink node at output layer, cluster then locates the closest cluster head. In this manner, cluster heads are used to execute resource-efficient healthcare data transmission from source to sink node, minimizing the latency. The obtained findings show that the suggested HPMIC-SIDCNL technique outperforms in terms of high delivery ratio with little packet loss and delay. The advantage of the proposed HPMIC-SIDCNL technique, it is simple to understand because it represents a solution to a given problem step by step and is not dependent on any programming language. Since everyone can understand it, even those without programming experience, it is simple.

Triboelectric Energy-Harvesting Floor Tile.

The aim of this study was to investigate the real-world electrical parameters that strongly affected the performance of a triboelectric energy-harvesting floor tile design: triboelectric material thickness, cover plate displacement distance or gap width, and cover plate pressing frequency, so that real-world specifications of the harvesting floor tile can be accurately specified. The structure of the designed triboelectric energy harvester, with readily available polytetrafluoroethylene (PTFE) film and aluminum foil, was simple and hence easy to fabricate, and the material cost was low. A square wave was used to simulate the pressing frequency on the test bench's cover plate. The results showed that the voltage and current were proportional to the gap width, and the thinner the triboelectric layer thickness, the higher the output voltage and current. A test bench with a 0.2 mm thick PTFE triboelectric layer generated the highest energy output. In a later experiment, a triboelectric energy-harvesting floor tile (TEHFT) prototype was constructed with 0.1 and 0.2 mm thick PTFE layers. We found that at 2 Hz stepping frequency and 0.1 mm PTFE thickness, the optimal load and cumulative energy of the TEHFT were 0.8 MΩ and 3.81 mJ, respectively, while with 0.2 mm PTFE thickness, these two parameters were 1.1 MΩ and 7.69 mJ, respectively. The TEHFT with 0.2 mm thick PTFE layer was able to illuminate a series of 100 to 150 LEDs, sufficient power to drive small electronics and sensor nodes. This discovery provides important data on the structure, material, and contact surface area of a TEHFT that can be adjusted to suit specific requirements of a special function triboelectric energy harvester.

EVALUATION OF LOW-COST DEPTH SENSORS FOR OUTDOOR APPLICATIONS

Abstract. Depth information is a key component that allows a computer to reproduce human vision in plenty of applications from manufacturing, to robotics and autonomous driving. The Microsoft Kinect has brought depth sensing to another level resulting in a large number of low cost, small form factor depth sensors. Although these sensors can efficiently produce data over a wide dynamic range of sensing applications and within different environments, most of them are rather suitable for indoor applications. Operating in outdoor areas is a challenge because of undesired illumination, usually strong sunlight or surface scattering, which degrades measurement accuracy. Therefore, after presenting the different working principle of existing depth cameras, our study aims to evaluate where two very recent sensors, the AD-FXTOF1-EBZ and the flexx2, stand towards the issue of outdoor environment. In particular, measurement tests will be performed on different types of materials subjected to various illumination in order to evaluate the potential accuracy of such sensors.

A multi-degree-of-freedom triboelectric energy harvester for dual-frequency vibration energy harvesting

Vibrational energy harvesting based on triboelectric transduction has been proven to be a cost-effective solution for powering small electronic sensors. Triboelectric energy harvesters (TEHs) that work in the contact-separation mode have been widely investigated with beam-mass structures. However, most beam-based TEHs utilize cantilever beams as their driving component, which is applicable only when its first mode is excited because higher modal frequencies are usually beyond the range of ambient vibrations. This study presents a novel contact-separation-mode energy harvester that, for the first time, combines triboelectric transduction with a multi-degree-of-freedom (MDOF) L-shaped beam-mass structure to harvest vibration energy at two operating frequencies. The TEH proposed in this study has two operating frequencies under 20 Hz and thus possesses an increased operating frequency range. A fully coupled electromechanical model that combines an MDOF distributed-parameter mechanical model with an electrical model for the TEH is derived. Experiments are then carried out to validate the model, characterize the performance of the TEH, and investigate the effect of the MDOF beam-mass structure on the contact-separation-mode TEH. It is shown that the predictions of the electromechanical model have an overall good agreement with the experimental results. Besides, the TEH can achieve a maximum root-mean-square voltage of 9.45 V when the first mode is excited and 11.56 V when the second mode is excited, given a base excitation acceleration of 0.6 g and the external load resistance of 1 MΩ. An optimal power of 300 μW is realized when the external load is 85 MΩ.

Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review.

Recent advances in sensor technology have facilitated the development and use of personalized sensors in monitoring environmental factors and the associated health effects. No studies have reviewed the research advancement in examining population-based health responses to environmental exposure via portable sensors/instruments. This study aims to review studies that use portable sensors to measure environmental factors and health responses while exploring the environmental effects on health. With a thorough literature review using two major English databases (Web of Science and PubMed), 24 eligible studies were included and analyzed out of 16,751 total records. The 24 studies include 5 on physical factors, 19 on chemical factors, and none on biological factors. The results show that particles were the most considered environmental factor among all of the physical, chemical, and biological factors, followed by total volatile organic compounds and carbon monoxide. Heart rate and heart rate variability were the most considered health indicators among all cardiopulmonary outcomes, followed by respiratory function. The studies mostly had a sample size of fewer than 100 participants and a study period of less than a week due to the challenges in accessing low-cost, small, and light wearable sensors. This review guides future sensor-based environmental health studies on project design and sensor selection.

Use of Laryngeal Sensors To Monitor Talk Time in People With Aphasia

<h3>Research Objectives</h3> To investigate the feasibility of using laryngeal sensors to monitor the amount of time people with aphasia talk in day-to-day life. <h3>Design</h3> Feasibility study. <h3>Setting</h3> Data was collected by the participants during an average day at home and in the community. <h3>Participants</h3> 10 participants with aphasia participated. Participants (mean [SD] age = 57.3 [5.5] years, 9 male) were diagnosed with aphasia following a left hemisphere stroke (Mean [SD] time post-onset = 57.2 [40.44] months). <h3>Interventions</h3> Participants were provided with all necessary equipment and given a training session by a speech language pathologist. Equipment included a small laryngeal sensor, adhesives, and an iPhone with a custom app to record the sensor data. Participants were trained to apply the sensor and connect to the app. Participants wore the sensor over the laryngeal notch of the throat for 8 hours a day for 1-3 days. Participants completed a structured interview about their experience with the sensors and activity during the data collection period. <h3>Main Outcome Measures</h3> Seconds of talk time per hour. <h3>Results</h3> Mean talk time per hour was 54.97 seconds (SD = 76.29 seconds). Sensor data was collected for a total of 22, 8 hour days. Participants were successful in setting up and using equipment in their home environment. Results of the structured interview reflect that the equipment was easy to use. <h3>Conclusions</h3> This study demonstrates the feasibility of measuring talk time in people with aphasia using a laryngeal sensor. The resulting data provides information about real world communication in this population and highlights their limited talk time. We discuss potential factors that may drive variability in talk time including aphasia characteristics (i.e., type, severity), demographic and environmental factors (gender, living arrangement, time spent at home). <h3>Author(s) Disclosures</h3> The authors have no conflicts to disclose.

X-ray performance of a small pixel size sCMOS sensor and the effect of depletion depth

In recent years, scientific Complementary Metal Oxide Semiconductor (sCMOS) devices have been increasingly applied in X-ray detection, thanks to their attributes such as high frame rate, low dark current, high radiation tolerance and low readout noise. We tested the basic performance of a backside-illuminated (BSI) sCMOS sensor, which has a small pixel size of 6.5 μm × 6.5 μm. At a temperature of -20°C, The readout noise is 1.6 e-, the dark current is 0.5 e-/pixel/s, and the energy resolution reaches 204.6 eV for single-pixel events. The effect of depletion depth on the sensor's performance was also examined, using three versions of the sensors with different deletion depths. We found that the sensor with a deeper depletion region can achieve a better energy resolution for events of all types of pixel splitting patterns, and has a higher efficiency in collecting photoelectrons produced by X-ray photons. We further study the effect of depletion depth on charge diffusion with a center-of-gravity (CG) model. Based on this work, a highly depleted sCMOS is recommended for applications of soft X-ray spectroscopy.

Calmodulin Binding Domains in Critical Risk Proteins Involved in Neurodegeneration.

Neurodegeneration leads to multiple early changes in cognitive, emotional, and social behaviours and ultimately progresses to dementia. The dysregulation of calcium is one of the earliest potentially initiating events in the development of neurodegenerative diseases. A primary neuronal target of calcium is the small sensor and effector protein calmodulin that, in response to calcium levels, binds to and regulates hundreds of calmodulin binding proteins. The intimate and entangled relationship between calmodulin binding proteins and all phases of Alzheimer's disease has been established, but the relationship to other neurodegenerative diseases is just beginning to be evaluated. Risk factors and hallmark proteins from Parkinson's disease (PD; SNCA, Parkin, PINK1, LRRK2, PARK7), Huntington's disease (HD; Htt, TGM1, TGM2), Lewy Body disease (LBD; TMEM175, GBA), and amyotrophic lateral sclerosis/frontotemporal disease (ALS/FTD; VCP, FUS, TDP-43, TBK1, C90rf72, SQSTM1, CHCHD10, SOD1) were scanned for the presence of calmodulin binding domains and, within them, appropriate binding motifs. Binding domains and motifs were identified in multiple risk proteins, some of which are involved in multiple neurodegenerative diseases. The potential calmodulin binding profiles for risk proteins involved in HD, PD, LBD, and ALS/FTD coupled with other studies on proven binding proteins supports the central and potentially critical role for calmodulin in neurodegenerative events.

Displacement Estimation Using 3D-Printed RFID Arrays for Structural Health Monitoring.

Radio frequency identification (RFID) tags are small, low-cost, wearable, and wireless sensors that can detect movement in structures, humans, or robots. In this paper, we use passive RFID tags for structural health monitoring by detecting displacements. We employ a novel process of using 3D printable embedded passive RFID tags within uniform linear arrays together with the multiple signal classification algorithm to estimate the direction of arrival using only the phase of the backscattered signals. We validate our proposed approach via data collected from real-world experiments using a unipolar RFID reader antenna and both narrowband and wideband measurements.

Magnetic field microscope using high-sensitivity giant magneto-resistance sensor with AC field modulation

A new high-sensitivity giant magneto-resistance (GMR) sensor system was developed and applied to a magnetic field microscope for which a magneto-impedance (MI) sensor was used as a high-sensitivity magnetic probe. The GMR sensor system achieved a detectivity of 13 pT/√Hz at 100 Hz. The sensing limit and spatial resolution were examined compared with the MI sensor. A 100 Hz AC magnetic field from a patterned Cu line was imaged. The GMR sensor showed better spatial resolution than that of the MI sensor owing to its small sensor unit size. The sensing limit was the same for both the GMR sensor and the MI sensor. This limit is due to ambient magnetic field noise. The GMR sensor could reduce this noise by differential detection using two sensor units. These results suggest that the GMR sensor system has advantages for application to the magnetic field microscopes compared with the MI sensor.

An improved biometric based user authentication and key agreement scheme for intelligent sensor based wireless communication

The new communication technologies have changed the traditional networks processes and provides more feasible, and convenient services for healthcare, agriculture, and transportation. Ad hoc networks devices have limited resources for energy, storage and processing. The conventional and traditional security solutions are not suitable for small and tiny sensor nodes. The symmetric cryptography method consumes more energy than public key cryptography method, whereas the public key cryptography method is a resource-intensive and not suitable for wireless networks. The symmetric cryptosystems have key management issues. Different authentication mechanisms have been designed but almost all of them are either complex or insecure. Improving network security with less resources consumption, there is a need to design and develop more advance and lightweight solution. This paper proposed a Biometric based User Authentication and Key Agreement Protocol (BUAKA) that uses a fuzzy extractor and one-way hash functions for sensor nodes authentication. The proposed scheme has four phases: initialization, registration, authentication, and password renewal phase. The proposed scheme resolves the complexities issues by providing a secure authentication mechanism and provide great resilience against a number of attacks. Finally, the proposed scheme is evaluated using several parameters such as security attacks, computational cost, and communication overhead to measure the effectiveness of proposed scheme.

Portable Quartz Crystal Resonator Sensor for Characterising the Gelation Kinetics and Viscoelastic Properties of Hydrogels

Hydrogel biomaterials have found use in various biomedical applications partly due to their biocompatibility and tuneable viscoelastic properties. The ideal rheological properties of hydrogels depend highly on the application and should be considered early in the design process. Rheometry is the most common method to study the viscoelastic properties of hydrogels. However, rheometers occupy much space and are costly instruments. On the other hand, quartz crystal resonators (QCRs) are devices that can be used as low-cost, small, and accurate sensors to measure the viscoelastic properties of fluids. For this reason, we explore the capabilities of a low-cost and compact QCR sensor to sense and characterise the gelation process of hydrogels while using a low sample amount and by sensing two different crosslink reactions: covalent bonds and divalent ions. The gelation of covalently crosslinked mucin hydrogels and physically crosslinked alginate hydrogels could be monitored using the sensor, clearly distinguishing the effect of several parameters affecting the viscoelastic properties of hydrogels, including crosslinking chemistry, polymer concentrations, and crosslinker concentrations. QCR sensors offer an economical and portable alternative method to characterise changes in a hydrogel material's viscous properties to contribute to this type of material design, thus providing a novel approach.

Artificial Neural Network-Based Abnormal Gait Pattern Classification Using Smart Shoes with a Gyro Sensor

Recently, as a wearable-sensor-based approach, a smart insole device has been used to analyze gait patterns. By adding a small low-power sensor and an IoT device to the smart insole, it is possible to monitor human activity, gait pattern, and plantar pressure in real time and evaluate exercise function in an uncontrolled environment. The sensor-embedded smart soles prevent any feeling of heterogeneity, and WiFi technology allows acquisition of data even when the user is not in a laboratory environment. In this study, we designed a sensor data-collection module that uses a miniaturized low-power accelerometer and gyro sensor, and then embedded it in a shoe to collect gait data. The gait data are sent to the gait-pattern classification module via a Wi-Fi network, and the ANN model classifies the gait into gait patterns such as in-toeing gait, normal gait, or out-toeing gait. Finally, the feasibility of our model was confirmed through several experiments.