Power Consumption Of Sensor Research Articles

Энергоэффективные газовые сенсоры на основе нанокристаллического оксида индия

The influence of grain size on the sensitivity of indium oxide with extremely small grains in range of 7–40 nm to nitrogen dioxide in low concentration at room temperature is investigated under the UV illumination. It is found that the sample with the intermediate size of nanocrystals shows the greatest response to nitrogen dioxide. The possibility of using nanocrystalline In2O3 to create a sensor for detecting NO2 in air at room temperature under UV illumination is demonstrated. It is shown that pulsed illumination may be used for NO2 detection at room temperature that significantly reduces the sensor power consumption.

Investigation of Stability and Power Consumption of an AlGaN/GaN Heterostructure Hydrogen Gas Sensor Using Different Bias Conditions.

A Pd-functionalized hydrogen gas sensor was fabricated on an AlGaN/GaN-on-Si heterostructure platform. The AlGaN layer under the Pd catalyst area was partially recessed by plasma etching, which resulted in a low standby current level enhancing the sensor response. Sensor stability and power consumption depending on operation conditions were carefully investigated using two different bias modes: constant voltage bias mode and constant current bias mode. From the stability point of view, high voltage operation is better than low voltage operation for the constant voltage mode of operation, whereas low current operation is preferred over high current operation for the constant current mode of operation. That is, stable operation with lower standby power consumption can be achieved with the constant current bias operation. The fabricated AlGaN/GaN-on-Si hydrogen sensor exhibited excellent sensing characteristics; a response of 120% with a response time of < 0.4 s at a bias current density of 1 mA/mm at 200 °C. The standby power consumption was only 0.54 W/cm2 for a sensing catalyst area of 100 × 24 μm2.

Multiwalled carbon nanotube based aromatic volatile organic compound sensor: sensitivity enhancement through 1-hexadecanethiol functionalisation.

Aromatic volatile organic compound (VOC) sensors are attracting growing interest as a response to the pressing market need for sensitive, fast response, low power consumption and stable sensors. Benzene and toluene detection is subject to several potential applications such as air monitoring in chemical industries or even biosensing of human breath. In this work, we report the fabrication of a room temperature toluene and benzene sensor based on multiwall carbon nanotubes (MWCNTs) decorated with gold nanoparticles and functionalised with a long-chain thiol self-assembled monolayer, 1-hexadecanethiol (HDT). High-resolution transmission electron microscopy (HRTEM) and Fourier transform infrared spectroscopy (FTIR) were performed to characterize the gold nanoparticle decoration and to examine the thiol monolayer bonding to the MWCNTs. The detection of aromatic vapours using Au-MWCNT and HDT/Au-MWCNT sensors down to the ppm range shows that the presence of the self-assembled layer increases the sensitivity (up to 17 times), selectivity and improves the response dynamics of the sensors.

Designing and analyzing two non-invasive current sensors using Ampere Force Law (AFL)

Here two different non-invasive current sensors are proposed, modeled and analyzed. The current sensors are based on the Ampere Force Law (AFL), defining the magnetic force between two parallel wire carrying currents. These current sensors can be used for detecting/sensing DC and AC currents as well as their combination in a single wire or multiple wires, and they do not rely on any permanent magnets for operation. In the first configuration, there are two microbeams, in which one of them is at the vicinity of the wire and undergoes the mechanical vibrations due to magnetic force between the wire and the microbeam. The movement of the microbeam while it is generating a magnetic field induces a current inside the another microbeam, which is stationary, as the output signal of the current sensor. In the second configuration, a single composite piezoelectric microbeam is used. The magnetic force between the wire and the piezoelectric microbeam leads the piezoelectric microbeam to move, thus it produces a voltage. Both configurations present an extremely low power consumption, which is not dependent on the sensitivity of the current sensors. The dynamic response, sensitivity and power consumption of the current sensors are investigated, compared and discussed.

Theoretical Modeling, Numerical Simulations and Experimental Study of Micro Thermal Convective Accelerometers

We present a one-dimensional (1D) theoretical model for the design analysis of a micro thermal convective accelerometer (MTCA). Systematical design analysis was conducted on the sensor performance covering the sensor output, sensitivity, and power consumption. The sensor output was further normalized as a function of normalized input acceleration in terms of Rayleigh number R $_{\mathrm {a}}$ (the product of Grashof number G $_{\mathrm {r}}$ and Prandtl number P $_{\mathrm {r}}$ ) for different fluids. A critical Rayleigh number ( R ac = 3,000) is founded, for the first time, to determine the boundary between the linear and nonlinear response regime of MTCA. Based on the proposed 1D model, key parameters, including the location of the detectors, sensor length, thin film thickness, cavity height, heater temperature, and fluid types, were optimized to improve sensor performance. Accordingly, a CMOS compatible MTCA was designed and fabricated based on the theoretical analysis, which showed a high sensitivity of 1,289 mV/g. Therefore, this efficient 1D model, one million times faster than CFD simulation, can be a promising tool for the system-level CMOS MEMS design.

Indirect Time-of-Flight Depth Sensor with Two-Step Comparison Scheme for Depth Frame Difference Detection.

A depth sensor with integrated frame difference detection is proposed. Instead of frame difference detection using light intensity, which is vulnerable to ambient light, the difference in depth between successive frames can be acquired. Because the conventional time-of-flight depth sensor requires two frames of depth-image acquisition with four-phase modulation, it has large power consumption, as well as a large area for external frame memories. Therefore, we propose a simple two-step comparison scheme for generating the depth frame difference in a single frame. With the proposed scheme, only a single frame is needed to obtain the frame difference, with less than half of the power consumption of the conventional depth sensor. Because the frame difference is simply generated by column-parallel circuits, no access of the external frame memory is involved, nor is a digital signal processor. In addition, we used an over-pixel metal–insulator–metal capacitor to store temporary signals for enhancing the area efficiency. A prototype chip was fabricated using a 90 nm backside illumination complementary metal–oxide–semiconductor (CMOS) image sensor process. We measured the depth frame difference in the range of 1–2.5 m. With a 10 MHz modulation frequency, a depth frame difference of >10 cm was successfully detected even for objects with different reflectivity. The maximum relative error from the difference of the reflectivity (white and wooden targets) was <3%.

Array of chemiresistors for single input multiple output (SIMO) variation-tolerant all printed gas sensor

The use of printed electronics in conjunction with nanomaterials such as carbon nanotubes (CNTs) proves to be an effective means for simple, efficient and cost-effective chemical and bio sensing applications due to their high surface area to volume ratio and other attractive properties. However, successful commercial implementation of nanosensors has been elusive due to inconsistent performance across the sensors, which translates to decreased credibility and reproducibility. Furthermore, the process uniformity in printed electronics is inferior to solid-state microfabrication technology as printed sensors suffer from material and process-induced variability at the current level of maturity. In order to take advantage of the promise of nanotechnology driven sensors, we herein propose an array of chemiresistors formed on one substrate as a single input multiple output (SIMO) sensor. Then, individual responses from sub-sensors in a SIMO sensor are aggregated and result in a statistical mean response to overcome variability issues. Random sampling was performed in this work from a sufficiently large population of CNT-based sub-sensors to define a representative set whose behavior closely mimics a larger array. The convergence of the population mean is proven from randomly chosen samples beyond a certain number of sub-sensors. An optimal strategy including sensor layout and power consumption is provided based on the resulting data analysis.

Design of Energy Management System using Autonomous Hybrid Micro-Grid under IOT Environment

In this paper, the automated power consumption unit is implemented by smart system technology. The interfacing could be done between the user and power sensing unit in which the collection of data is segregated and processed by the common interface bridge unit. Then, the data is transmitted to the power grid unit where the the processing and sensing power consumption is executed for the consumer in aspects of bill generation. All this kind of environment is controlled by the Internet of Things (IoT) substances and regulated by the configured power supplies. The entire setup wire diagram is simulated using Lab VIEW. The another advantage of the proposed system is equipped with highly cyber security point

Self-Sustaining Acoustic Sensor With Programmable Pattern Recognition for Underwater Monitoring

Minimizing the power consumption of always-on sensors is crucial for extending the lifetime of battery-operated devices that are required to monitor events continuously and for long periods. This paper proposes a novel programmable $\mu \text{W}$ event-driven acoustic detector featuring “always-on” audio pattern recognition. The event-driven detector detects up to eight programmable spectral–temporal features extracted with a low-power single-channel analog circuit and classifies the features by an onboard microcontroller. The event-driven detector is combined with novel microbial fuel cells (MFCs) to achieve self-sustainability in an underwater scenario. Experimental results demonstrate that the power consumption of the detector is only $26.89~\mu \text{W}$ during always-on mode, achieving up to 59-dB sound pressure level of sensitivity. High detection accuracy of up to 95.89% in recognizing acoustic patterns has been experimentally verified. Accurate measurements with commercial MFCs demonstrate the capability to achieve self-sustainability in always-on monitoring.

Low Power In Situ AI Calibration of a Three-Axial Magnetic Sensor

Magnetic surveys are conventionally performed by scanning a domain with a portable scalar magnetic sensor. Unfortunately, scalar magnetometers are expensive, power consuming and bulky. In many applications, calibrated vector magnetometers can be used to perform magnetic surveys. In recent years algorithms based on artificial intelligence (AI) achieve state-of-the-art results in many modern applications. In this work we investigate an AI algorithm for the classical scalar calibration of magnetometers. A simple, low cost method for performing a magnetic survey is presented. The method utilizes a low power consumption sensor with an AI calibration procedure that improves the common calibration methods and suggests an alternative to the conventional technology and algorithms. The setup of the survey system is optimized for quick deployment in-situ right before performing the magnetic survey. We present a calibration method based on a procedure of rotating the sensor in the natural earth magnetic field for an optimal time period. This technique can deal with a constant field offset and non-orthogonality issues and does not require any external reference. The calibration is done by finding an estimator that yields the calibration parameters and produces the best geometric fit to the sensor readings. A comprehensive model considering the physical, algorithmic and hardware properties of the magnetometer of the survey system is presented. The geometric ellipsoid fitting approach is parametrically tested. The calibration procedure reduced the root-mean-squared noise from the order of 104 nT to less than 10 nT with variance lower than 1 nT in a complete 360 degrees rotation in the natural earth magnetic field.

Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device

In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the human body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and latency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gateway-centric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper explores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM’s big.little™ multicore for different signal dimension and allocated computational resources. Experimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms.

Magnetic Signature Analysis for Smart Security System Based on TMR Magnetic Sensor Array

This paper presents a novel low power security system based on magnetic anomaly detection by using tunneling magnetoresistance (TMR) magnetic sensors. In this paper, a smart light has been developed, which consists of TMR sensors array, detection circuits, a micro-controller, and a battery. Taking the advantage of low power consumption of TMR magnetic sensors, the smart light powered by Li-ion battery can work for several months. Power spectrum density of the obtained signal was analyzed to reject background noise and improve the signal-to-noise ratio effectively by 1.3 dB, which represented a 30% detection range improvement. Also, by sending the signals to PC, the magnetic fingerprints of the objects have been configured clearly. In addition, the quick scan measurement has been also performed to demonstrate that the system can discriminate the multiple objects with 30 cm separation. Since, the whole system was compact and portable; it can be used for security check at office, meeting room, or other private places without attracting any attention. Moreover, it is promising to integrate multiply such systems together to achieve a wireless security network in large-scale monitoring.

A low-power sensor polling for aggregated-task context on mobile devices

Current smartphones are very rich in the type of sensors to provide various user-behavior tracing and social network sharing, i.e., pervasive social networking applications. Sensor power consumption contributes a significant part of overall power budget in system since each mobile application always actives the related sensors without any restriction. Even worse, in-smartphone sensors are usually activated with full frequencies to match the flushing rates of a group of aggregated tasks, known as full polling-based detection, which results in significant unnecessary activities on sensors and fast battery sucking-up. In this work, we propose a low-power sensor polling strategy for mobile applications to dynamically remove unnecessary sensor activities. With this design, the unrelated sensors can keep in sleeping status for longer time. To schedule mobile sensors, we provide sample-based scheduler as a middleware to model the on-the-fly mathematical relationship between application invoking and sensor real activities. Thus, the scheduler is able to dynamically configure sensor flushing rates under various application context that isexecuted by users. We evaluate this framework with a wide range of mobile applications. The results show that our new low-power scheduler spends a tiny responding delay (97ms) in the middleware, as the overhead, to reduce 70% sensor energy consumption, comparing with the conventional exhausting detecting operation.

Biomedical health monitoring system design and analysis

&lt;span&gt;e-Health remote monitoring systems have bloomed rapidly with a myriad of applications. This paper discusses a design of a remote monitoring device for biomedical field. Four biomedical sensors which are electrocardiography (ECG), airflow, galvanic skin response and temperature with two boards which are the e-Health Shield Board V2.0 and Arduino Uno Board are used. The results show satisfactory output for each experiment using two test subjects. The device able to achieve high accuracy where percentage of temperature difference is less than 1% compared to the commercial devices with an average power consumption of each working sensor on board is ≤9W.&lt;/span&gt;

Transformer Health Management Based on Self-Powered RFID Sensor and Multiple Kernel RVM

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer.

A Fully Integrated Wireless Flexible Ammonia Sensor Fabricated by Soft Nano-Lithography.

Flexible ammonia (NH3) sensors based on one-dimensional nanostructures have attracted great attention due to their high flexibility and low power consumption. However, it is still challenging to reliably and cost-effectively fabricate ordered nanostructure-based flexible sensors. Herein, a smartphone-enabled fully integrated system based on a flexible nanowire sensor was developed for real-time NH3 monitoring. Highly aligned, sub-100 nm nanowires on a flexible substrate fabricated by facile and low-cost soft lithography were used as sensitive elements to produce impedance response. The detection signals were sent to a smartphone and displayed on the screen in real time. This nanowire-based sensor exhibited robust flexibility and mechanical durability. Moreover, the integrated NH3 sensing system presented enhanced performance with a detection limit of 100 ppb, as well as high selectivity and reproducibility. The power consumption of the flexible nanowire sensor was as low as 3 μW. By using this system, measurements were carried out to obtain reliable information about the spoilage of foods. This smartphone-enabled integrated system based on a flexible nanowire sensor provided a portable and efficient way to detect NH3 in daily life.

Energy-Efficient Photoplethysmogram Compression to Estimate Heart and Respiratory Rates Simultaneously

A photoplethysmogram (PPG) sensor has been broadly used for smart watches and bands because it is easy to measure and contains many health information, such as heart rate (HR) and respiratory rate (RR). Because the PPG sensor blinks an LED at sampling instants, the rate for sampling and LED flashing should be reduced to extend the battery life. To reduce the rate, we employed two different compressive covariance sensing (CCS) techniques and applied them to HR and RR estimation. The CCS cannot recover a signal itself but reconstructs its covariance. We designed a signal processing technique to extract HR and RR from the reconstructed covariance. The estimation performance was evaluated by using the open-source data and the experimental data, and the power consumption of a wrist-type PPG sensor with respect to the compression ratio was evaluated. The proposed method acquired the covariance of the PPG with an average sampling rate of 1.79 Hz below the Nyquist rate for HR (10 Hz), and it significantly reduced the energy consumption for the PPG sampling. Moreover, its estimation accuracy was sufficient to be used for a wearable healthcare system. As a result, the proposed method showed that HR and RR can be estimated with an ultra-low-power PPG sensor.

Cloud-Aided Cognitive Ambient Backscatter Wireless Sensor Networks

Cognitive ambient backscatter is a wireless communication paradigm that allows a secondary backscatter device to superimpose its information-bearing data on a primary signal, without requiring any type of power-consuming active components or other signal conditioning units. In such a network, the performance of the backscatter system can be severely degraded by channel estimation errors and co-channel direct-link interference (DLI) from the primary system. To overcome these shortcomings, we consider a cloud radio access network (C-RAN) architecture, where both the primary and secondary edge nodes are connected to a cloud processor via high-speed links. In this centralized architecture, secondary edge nodes provide network access to ambient backscatter passive and semi-passive sensors with communication capabilities, and the problem of acquiring channel state information and suppressing the DLI is managed by the cloud processor. In particular, we assess the performance of the secondary backscatter sensor transmission in a realistic system setup, which takes into account training-based channel estimation, practical modulation constraints, and imperfect DLI suppression. In addition, we formulate and solve an optimization problem aimed at maximizing the transmission rate of the secondary transmission, subject to limits on channel estimation error, average symbol error rate, power consumption, and energy storage capabilities of the backscatter sensor. The validity of our analysis and the performance of the secondary system based on the proposed designs are corroborated through the Monte Carlo simulations.

Evaluation of the Possible Use of PPG Waveform Features Measured at Low Sampling Rate

To date, the applications of photoplethysmograms in the estimation of cuff-less blood pressure, arteriosclerosis, and so on have been studied. Photoplethysmogram waveform features have often been used to estimate the target volumes. For these estimations, it is necessary to acquire photoplethysmogram waveforms and changes in their derivative waveform details, for which the photoplethysmograms measured at comparatively high sampling rates have been used. The performance of smartphone cameras and wearable photoplethysmogram sensors has improved; with regard to mobile health technology, photoplethysmograms measured at lower sampling rates offer considerable advantages. These include lower computational resources, compression of accumulated data, and lower sensor power consumption. However, compared to photoplethysmograms measured at a high sampling rate, photoplethysmogram measurement at a low sampling rate will result in waveform signal degradation. This paper investigates the possibility of using photoplethysmograms measured at a low sampling rate. To this end, we statistically compared photoplethysmogram waveform features obtained from 63 male subjects free of circulatory diseases, at a sampling rate of 240 Hz, with waveform features obtained at low sampling rates (120, 60, 30, 20, and 10 Hz) through downsampling, and evaluated possible commercial use.

Quality of Service and Power Consumption Optimization on the IEEE 802.15.4 Pulse Sensor Node based on Internet of Things

Abstract—The Purpose of this research is to determine the Quality of Service (QoS) Zigbee or IEEE 802.15.4 sensor Node use the indicators, i.e. the Receiver Signal Strength and PathLoss (attenuation (-dB)) at the time of communication of the sensor node end device to the sensor Router node or Coordinator sensor node (sink). The factor power consumption sensor node is important to maintain the lifetime sensor node, The Sensor data in this research is the pulse sensor. The development of the Wireless Sensor Network communication system is in multi-hop communication, with efforts to obtain low power consumption on each sensor node. This study utilizes the Routing Protocol for Low Power and Lossy Network (RPL) method with position management on the sensor node on DODAGs consequently, that the average power consumption value for each sensor node is low. The benefit of the Sensor node is to send pulse sensor data from various nodes that are interconnected at different distances in multi-hop so that power consumption and Quality of Services (QoS) can be identified from the sensor node. From the research results, the average PathLoss value of IEEE 802.15.4 or Zigbee in free space is obtained by comparing the various simulation values and field experiments at a distance of 50 m at -75.4 dB and the Average Receiver Signal Strength (RSS) with a comparison of Equation and Experiments in the field with parameters The minimum Power Transmitter is 0 dBm and the Power Transmitter is maximum +20 dBm at a distance of 50m at - 66.6 dBm. Therefore, Pulse Sensor data will be displayed on the Web Page and stored in the MySQL database using Raspberry PI 3 as the Internet Gateway.