Power Sensor Research Articles

A robust electrochemical sensing platform for the detection of erlotinib based on nitrogen-doped graphene quantum dots/copper nanoparticles-polyaniline-graphene oxide nanohybrid

Erlotinib is a potent and highly specific tyrosine kinase inhibitor with the hindering effects on the growth of cancer cells. An electrochemical sensor with the great sensitivity and selectivity was fabricated for determining erlotinib by using a graphite rod electrode modified by the nitrogen-doped graphene quantum dots (N-GQDs) and a ternary nanohybrid comprising copper nanoparticles, polyaniline, along with graphene oxide (N-GQDs/CuNPs-PANI@GO) for the first time. The establishment of PANI and CuNPs was done simultaneously on the GO surface by the in situ oxidative polymerization method. The morphological characteristics and elemental structure of the synthesized nanoparticles were examined by some microscopy techniques and x-ray energy/diffraction methods. The fabricated sensor represented the electrocatalytic activity towards erlotinib with a linear detection range from 1.0 nM to 35.0 μM, a detection limit of 0.712 nM, and a sensitivity of 1.3604 μA μM−1. Moreover, the N-GQDs/CuNPs-PANI@GO sensor showed acceptable stability up to 30 d (94.82%), reproducibility (RSD values of 3.19% intraday and 3.52% interday), and repeatability (RSD value of 3.65%) as a novel and powerful electrochemical sensor. It was successfully applied to monitor erlotinib in the drug-injected aqueous solution, serum, and urine samples that proved the capability of the sensor for the erlotinib monitoring in the biological samples.

An Online Model to Minimize Energy Consumption of IoT Sensors in Smart Cities

The Internet of Things (IoT), which allows systems of billions or trillions of “things,” such as sensors, to communicate with each other over the Internet, is encountering several technical and application challenges. One of these challenges is that IoT sensors send redundant and self-similar data to the edge gateways consuming a large amount of energy and making it extremely difficult to obtain an appropriate network lifetime, which has become a bottleneck in scaling such applications. To address these issues, we propose a new solution based on powering sensors using artificial intelligence to make smart decisions about transmitting collected readings. We take advantage of autocorrelation (AC) to detect self-similarity and propose updating mechanism that employs deep reinforcement learning (RL). Our proposed model is a real-time model that can determine the redundant data and self-similarity and then make the smart decision about transmitting data. We evaluate our proposed solution using measurements obtained from Queanbeyan smart city, Australia, and available-public dataset and show that our proposed model can reduce the amount of transmitted data and minimize the power consumption of sensors.

Low Power Environmental Image Sensors for Remote Photogrammetry.

This paper aims to prove the feasibility of a 4D monitoring solution (3D modeling and temporal monitoring) for the sandbar and to characterize the species’ role in the landscape. The developed solution allows studying the interaction between the river dynamics and vegetation using a network of low resolution and low power sensors. The issues addressed concern the feasibility of implementing a photogrammetry solution using low-resolution sensors as well as the choice of the appropriate sensor and its testing according to different configurations (image capture and storage on the sensor and/or image transmission to a centralization node) and also the detailed analysis of the different phases of the process (camera initialization, image capture, network transmission and selection of the most appropriate standby mode). We reveal that the tiny, low-cost board (ESP32-Cam) can perform a 3D reconstruction and propose using the camera’s UXGA (1600, 1200) resolution because of the quality rendering and energy consumption. A multi-node scenario based on a combined Wi-Fi and GSM relay is proposed in the study showing several years of autonomy for the system. Finally, to illustrate the energy cost of the module, we have defined a study process, where we have identified and quantified one by one the different phases of operation of the card for better energy optimization (setup, camera configuration, shooting, saving on SD card, or sending by Wi-Fi). The device is now operational for deployment on the Allier River (France).

New Advances in Lateral Flow Immunoassay (LFI) Technology for Food Safety Detection.

With the continuous development of China’s economy and society, people and the government have higher and higher requirements for food safety. Testing for food dopants and toxins can prevent the occurrence of various adverse health phenomena in the world’s population. By deploying new and powerful sensors that enable rapid sensing processes, the food industry can help detect trace adulteration and toxic substances. At present, as a common food safety detection method, lateral flow immunochromatography (LFI) is widely used in food safety testing, environmental testing and clinical medical treatment because of its advantages of simplicity, speed, specificity and low cost, and plays a pivotal role in ensuring food safety. This paper mainly focuses on the application of lateral flow immunochromatography and new technologies combined with test strips in food safety detection, such as aptamers, surface-enhanced Raman spectroscopy, quantum dots, electrochemical test strip detection technology, biosensor test strip detection, etc. In addition, sensing principles such as fluorescence resonance energy transfer can also more effective. Different methods have different characteristics. The following is a review of the application of these technologies in food safety detection.

An Intelligent Control Scheme for Optimum Efficiency and Reduced Emission Operation of Marine Transportation System

This paper proposes an intelligent control scheme (ICS) for optimum efficiency and reduced emission operation in small marine transportation systems such as tugs, research vessels, and small scale cruiser ships. The diesel engines used in marine and other transport related applications are designed to provide optimum efficiency at a specific value of mechanical loading. Generally, these engines provide the best fuel efficiency near 80% of their rated load and also the break specific emission of CO (carbon mono oxide) and other harmful greenhouse gases are found to be lowest at this specific amount of mechanical loading. The proposed controller makes the diesel engine to operate continuously at 80% of loading in order to ensure an optimum efficiency operation even if the mechanical load is kept changing due to the variations and fluctuations in the ship propulsion load or electrical load. The intelligent control scheme (ICS) uses a battery bank as an energy balancing element to maintain a constant total mechanical loading on the engine shaft using a bidirectional power flow control through the battery bank thus changing effective mechanical loading on the engine for optimum efficiency operation of the marine transportation system. The output mechanical power (P) delivered by the engine on its shaft is sensed using a power sensor and compared with its optimum value in order to calculate the error. Thereafter, based on the information about the error in engine shaft power, the ICS regulates the power flow to the SPAG (single phase asynchronous generator) in such a manner that the mechanical load on the engine is restored to its optimum value immediately. The optimum efficiency operation is maintained under randomly varying mechanical power demanded by the ship propulsion system and it is also maintained under varying electrical loads in the marine transportation system. This ICS scheme has been implemented in the laboratory using a DSP controller. The detailed test results are presented to validate the claims.

Modeling on RF Circuit and Thermal Conduction of a MEMS Amplitude Demodulator

An amplitude demodulator, using an on-line MEMS power sensor is studied in this paper. The model of the equivalent circuit of on-line structure is solved, and the heat transfer model related to the thermopile is proposed and also simulated to verify the feasibility of AM demodulation. Experiments are also performed to verify the proposed models. The microwave performance shows that the device has a return loss that is better than 20 dB, and an insertion loss of less than 0.5 dB up to 10 GHz. Both the model and the experimental results show that the MEMS device can perform demodulation of an AM signal with carrier frequency of 0.01–10 GHz and signal frequency of under 1kHz. [2021-0261]

Massive MIMO Empowered Wireless Powered Sensor Networks: An Optimal Design With Statistical CSI

Wireless energy transfer (WET) has been anticipated as a viable solution to combat the challenges of scarce energy supply in Internet-of-Things (IoT) applications. To reap the benefit brought by large scale antenna arrays, massive multiple-input-multiple-out (MIMO) empowered wireless powered sensor networks (WPSNs) is investigated in this letter to meet the urgent demand for reliable and self-sustainable IoT applications. Considering a large-scale antenna array deployed at the access point for energy transfer to and information collection from the multiple-antenna wireless powered sensors, the hardening property in energy transfer is generalized and theoretically proved based on random matrix theory. Based on the hardening properties, the harvesting energy and the uplink sum-rate for sensor data collection are found to be asymptotically deterministic and depend on the statistical channel state information (CSI) only. The hardening properties in both energy and information transfers enable a novel optimal design on the WPSNs based on statistical CSI only. Particularly, the designs of the downlink energy beamforming and multiple uplink information beamforming can be decoupled, and global optimal solutions for the beamforming and time allocation to maximize the uplink sum-rate given downlink power constraint are found in semi-closed forms. Different from prior WPSNs designs, statistical CSI but not instantaneous CSI is exploited and frequent channel estimation and heavy communication overhead can be avoided with a much lower computation complexity, thus improving the practicability and sustainability of the WPSNs. The effectiveness of the proposed design is finally demonstrated by numerical simulations.

ZBP1: A Powerful Innate Immune Sensor and Double-Edged Sword in Host Immunity.

Z-conformation nucleic acid binding protein 1 (ZBP1), a powerful innate immune sensor, has been identified as the important signaling initiation factor in innate immune response and the multiple inflammatory cell death known as PANoptosis. The initiation of ZBP1 signaling requires recognition of left-handed double-helix Z-nucleic acid (includes Z-DNA and Z-RNA) and subsequent signaling transduction depends on the interaction between ZBP1 and its adapter proteins, such as TANK-binding kinase 1 (TBK1), interferon regulatory factor 3 (IRF3), receptor-interacting serine/threonine-protein kinase 1 (RIPK1), and RIPK3. ZBP1 activated innate immunity, including type-I interferon (IFN-I) response and NF-κB signaling, constitutes an important line of defense against pathogenic infection. In addition, ZBP1-mediated PANoptosis is a double-edged sword in anti-infection, auto-inflammatory diseases, and tumor immunity. ZBP1-mediated PANoptosis is beneficial for eliminating infected cells and tumor cells, but abnormal or excessive PANoptosis can lead to a strong inflammatory response that is harmful to the host. Thus, pathogens and host have each developed multiplex tactics targeting ZBP1 signaling to maintain strong virulence or immune homeostasis. In this paper, we reviewed the mechanisms of ZBP1 signaling, the effects of ZBP1 signaling on host immunity and pathogen infection, and various antagonistic strategies of host and pathogen against ZBP1. We also discuss existent gaps regarding ZBP1 signaling and forecast potential directions for future research.

Fabrication and characterization of a highly sensitive hydrogen gas sensor

A rapidly growing world where sources are decreasing mmoneoney, time causes a big problem for some region. Contaminations get expanding step by step because of the contamination and waste that comes from businesses in light of petroleum derivative. To diminish the utilization of petroleum products around then a characteristic wellspring of energy went about as a deliverer which was not just utilized as a perfect wellspring of energy yet in addition utilized in different fuel and modern cycles. Such gas is hydrogen gas which is the most ideal decision likewise universally acknowledged and present in bounty sum. Yet, hydrogen gas is exceptionally combustible and as its particles are minuscule in size, its scentless, vapid property makes it hard to perceive and recognize effectively in the event that any measure of gas is releasing, such little leakage can bring about a major debacle and its impact stay for a significant stretch. To forestall and recognize such leakage and mishap a sensor should be planned and creation is fundamental. This paper talks about different materials, strategies, and cycles expected to make a viable detecting gadget. Likewise, the entire cycle stream and other plan boundaries are examined with an alternate writing review of paper to make a powerful and valuable sensor for the identification of Hydrogen gas.

Robust and Accurate Fine-Grain Power Models for Embedded Systems With No On-Chip PMU

This letter a novel approach to event-based power modeling for embedded platforms that do not have a performance monitoring unit (PMU). The method involves complementing the target hardware platform, where the physical power data is measured, with another platform on which the CPU performance data, that is needed for model generation, can be collected. The methodology is used to generate accurate fine-grain power models for the Gaisler GR712RC dual-core LEON3 fault-tolerant SPARC processor with onboard power sensors and no PMU. A Kintex UltraScale field-programmable gate array (FPGA) is used as the support platform to obtain the required CPU performance data, by running a soft-core representation of the dual-core LEON3 as on the GR712RC but with a PMU implementation. Both platforms execute the same benchmark set and data collection is synchronized using per-sample timestamps so that the power sensor data from the GR712RC board can be matched to the PMU data from the FPGA. The synchronized samples are then processed by the Robust Energy and Power Predictor Selection (REPPS) software in order to generate power models. The models achieve less than 2% power estimation error when validated on an industrial use case and can follow program phases, which makes them suitable for runtime power profiling during development.

Power to Pulse Width Modulation Sensor for Remote Power Analysis Attacks

Field-programmable gate arrays (FPGAs) deployed on commercial cloud services are increasingly gaining popularity due to the cost and compute benefits offered by them. Recent studies have discovered security threats than can be launched remotely on FPGAs that share the logic fabric between trusted and untrusted parties, posing a danger to designs deployed on cloud FPGAs. With remote power analysis (RPA) attacks, an attacker aims to deduce secret information present on a remote FPGA by deploying an on-chip sensor on the FPGA logic fabric. Information captured with the on-chip sensor is transferred off the chip for analysis and existing on-chip sensors demand a significant amount of bandwidth for this task as a result of their wider output bit width. However, attackers are often left with the only option of using a covert communication channel and the bandwidth of such channels is generally limited. This paper proposes a novel area-efficient on-chip power sensor named PPWM that integrates a logic design outputting a pulse whose width is modulated by the power consumption of the FPGA. This pulse is used to clear a flip-flop selectively and asynchronously, and the single-bit output of the flip-flop is used to perform an RPA attack. This paper demonstrates the possibility of successfully recovering a 128-bit Advanced Encryption Standard (AES) key within 16,000 power traces while consuming just 25% of the bandwidth when compared to the state of the art. Moreover, this paper assesses the threat posed by the proposed PPWM to remote FPGAs including those that are deployed on cloud services.

Potential and challenges of tool condition monitoring in gear hobbing

Due to its high productivity, gear hobbing is one of the most frequently used manufacturing processes for the soft machining of cylindrical gears. One of the main objectives of an optimized manufacturing process is to maintain the required component quality while minimizing manufacturing costs. In both cases, knowledge of the tool wear is of great importance. Tool Condition Monitoring (TCM) provides a methodical approach to tracking tool wear during the process. In this report, various sensors are investigated with regard to their potential for TCM. Fly-cutting and hobbing experiments were conducted for this purpose. The signal data recorded during the tests were processed using high-pass filters, Hilbert transforms and Fast Fourier Transforms (FFT) in the time and frequency domain and evaluated according to various parameters. Based on the results, statements were made about the relationships between signal data and process conditions. For a precise evaluation of the tool condition, the combination of several sensors is necessary. In particular, the tool-side-mounted acoustic emission and acceleration sensors in combination with the power sensor and the airborne sound sensor showed increased amplitude values with increased wear. For the acceleration signals it could be shown that higher orders reacted more sensitively to increased tool wear. For the workpiece-side-mounted sensors, no meaningful results could be obtained due to the large distance to the cutting zone.

A low cost UV-IR dual wavelength optical sensor with Chirp modulation for in-situ chemical oxygen demand measurements

Chemical oxygen demand (COD) is one of the most representative indicators in water quality monitoring. Among numerous methodologies of COD measurement, optical method has received increasing interests as it can avoid secondary contamination and the frequent replacement of electrodes. However, different with the evolving progress of Infrared (IR) based turbidity sensor, the optical measurements of COD with ultraviolet (UV) absorption still rely on the complex high-end hardware architecture to achieve high accuracy, which prevent its wide deployment in the large scale monitoring of fresh waters. Nonetheless, the essential reason is the weak and in-stable UV signals after absorption. This motivates numerous method in optic domain to enhance the Signal-to-Noise Ratios (SNR) of UV signal, which, as a trade off, have to involve the complex and precise hardware architecture. This paper chooses to ease this challenge from the digital domain, where the Chirp modulation has been applied to both UV and IR signals to form a UV-IR dual wavelength COD measurement system. With Chirp modulated signals, the effective SNRs have been effectively improved due to the convolution gain, while the cross interference of dual-wavelength has been avoided as well with the higher time resolution. Benefited by this simple architecture, a low cost, low power, and high-precision COD sensor has be designed to support the large scale in-Situ deployment.

Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest.

Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.

Nucleus-targeting pH-Responsive carbon dots for fast nucleus pH detection

Nucleus pH is closely linked to many diseases such as aging, heart disease, skeletal myopathies, cancer, Alzheimer's disease, etc. Nevertheless, fluorescent sensors that can directly monitor nucleus pH changes have not yet been reported. Here, we first reported a green emissive carbon dots (CDs) for nucleus pH detection in living cells. CDs can selectively target nucleus with high accumulation at nucleolus due to their high affinity towards RNA once entering cells by lipid raft mediated endocytosis. Without washing, CDs at 5 μg/mL was enough to lighten nucleus within 10 min with the fluorescence on ever after 24 h incubation, achieving fast, wash-free, and long-term nucleus/nucleolus imaging. Meanwhile, the luminescent intensity of CDs was reduced gradually when pH changed continuously from 1 to 12, showing a pH-responsive fluorescence property with two linear ranges of pH 2–7 and pH 7–12. With their nucleus-targeting ability and pH-dependent photoluminescent property, CDs was successfully leveraged for nucleus pH detection in A549 cells and for in vivo pH sensing in zebra fish. CDs present a promising and powerful fluorescent sensor for nucleus imaging and nucleus pH sensing in living cells on the way to understand nucleus-related biological events.

A Truck-Borne System Based on Cold Atom Gravimeter for Measuring the Absolute Gravity in the Field.

The cold atom gravimeter (CAG) has proven to be a powerful quantum sensor for the high-precision measurement of gravity field, which can work stably for a long time in the laboratory. However, most CAGs cannot operate in the field due to their complex structure, large volume and poor environmental adaptability. In this paper, a home-made, miniaturized CAG is developed and a truck-borne system based on it is integrated to measure the absolute gravity in the field. The measurement performance of this system is evaluated by applying it to measurements of the gravity field around the Xianlin reservoir in Hangzhou City of China. The internal and external coincidence accuracies of this measurement system were demonstrated to be 35.4 μGal and 76.7 μGal, respectively. Furthermore, the theoretical values of the measured eight points are calculated by using a forward modeling of a local high-resolution digital elevation model, and the calculated values are found to be in good agreement with the measured values. The results of this paper show that this home-made, truck-borne CAG system is reliable, and it is expected to improve the efficiency of gravity surveying in the field.

Soft, Pressure-Tolerant, Flexible Electronic Sensors for Sensing under Harsh Environments.

Energy-efficient, miniaturized electronic ocean sensors for monitoring and recording various environmental parameters remain a challenge because conventional ocean sensors require high-pressure chambers and seals to survive the large hydrostatic pressure and harsh ocean environment, which usually entail a high-power supply and large size of the sensor system. Herein, we introduce soft, pressure-tolerant, flexible electronic sensors that can operate under large hydrostatic pressure and salinity environments, thereby eliminating the need for pressure chambers and reducing the power consumption and sensor size. Using resistive temperature and conductivity (salinity) sensors as an example for demonstration, the soft sensors are made of lithographically patterned metal thin films (100 nm) encapsulated with soft oil-infused elastomers and tested in a customized pressure vessel with well-controlled pressure and temperature conditions. The resistance of the temperature and pressure sensors increases linearly with a temperature range of 5-38 °C and salinity levels of 30-40 Practical Salinity Unit (PSU), respectively, relevant for this application. Pressure (up to 15 MPa) has shown a negligible effect on the performance of the temperature and salinity sensors, demonstrating their large pressure-tolerance capability. In addition, both temperature and salinity sensors have exhibited excellent cyclic loading behaviors with negligible hysteresis. Encapsulated with our developed soft oil-infused elastomer (PDMS, poly(dimethylsiloxane)), the sensor has shown excellent performance under a 35 PSU salinity water environment for more than 7 months. The soft, pressure-tolerant and noninvasive electronic sensors reported here are suitable for integration with many platforms including animal tags, profiling floats, diving equipment, and physiological monitoring.

Numerical demonstration of a dispersion engineered metallic metasurface assisted mm-wave wireless sensor

In this paper, a metasurface-assisted multiport wireless power sensor is proposed and numerically verified for wireless power transfer (WPT) applications at mm-wave frequency band. A fully metallic 2D Luneburg lens constructed using glide symmetric unit cells, with a maximum gain of 18 dBi, acts as the radiating structure to receive the input RF power with a wide angular coverage range of ±70°. A set of optimized class F rectifiers are integrated with this multiport lens using waveguide to microstrip transitions to obtain high power conversion efficiency over a wide angular space. These rectifying circuits are further connected for DC power combining, and a maximum power conversion efficiency of 72% is obtained at an input power level of 15.8 dBm.

Self-Sufficient Sensor Node Embedding 2D Visible Light Positioning through a Solar Cell Module

Nowadays, indoor positioning (IP) is a relevant aspect in several scenarios within the Internet of Things (IoT) framework, e.g., Industry 4.0, Smart City and Smart Factory, in order to track, amongst others, the position of vehicles, people or goods. This paper presents the realization and testing of a low power sensor node equipped with long range wide area network (LoRaWAN) connectivity and providing 2D Visible Light Positioning (VLP) features. Three modulated LED (light emitting diodes) sources, the same as the ones commonly employed in indoor environments, are used. The localization feature is attained from the received light intensities performing optical channel estimation and lateration directly on the target to be localized, equipped with a low-power microcontroller. Moreover, the node exploits a solar cell, both as optical receiver and energy harvester, provisioning energy from the artificial lights used for positioning, thus realizing an innovative solution for self-sufficient indoor localization. The tests performed in a ~1 m2 area reveal accurate positioning results with error lower than 5 cm and energy self-sufficiency even in case of radio transmissions every 10 min, which are compliant with quasi-real time monitoring tasks.

Two kinds of biomass-derived carbon dots with one-step synthesis for Fe3+ and tetracyclines detection

In this study, we developed an eco-friendly approach by utilizing industrial hemps as the carbon source to synthesize two kinds of CDs by one-pot hydrothermal method. We observed that the excitation wavelength showed dependence on CDs concentration in small CDs (S-CDs), not in big CDs (B-CDs). Further study showed that the B-CDs can be utilized for detection Fe3+ with the detection limit as low as 0.47 μmol L−1. Instead, our S-CDs could be specifically identified tetracycline antibiotics (TC, CTC, OTC) with the detection limits as 0.0330 μmol L−1, 0.27 μmol L−1 and 0.28 μmol L−1, respectively. In addition, we have successfully applied their application in actual sample (bottled drinking water and facial mask) to detect Fe3+ and TC, indicating their practicality. Overall, we have demonstrated that B-CDs and S-CDs detected Fe3+ and tetracycline antibiotics as powerful sensors and their potential applications in food and cosmetics detection of Fe3+ and TC.