Small Dynamic Range Research Articles

A Simple Combination Method of MZI and MI for Salinity Measurement

Generally, a sensing structure with high sensitivity has a small dynamic measurement range and vice versa. A simple combination method of Mach-Zehnder-interferometer (MZI) and a modal interference (MI) for salinity measurement is studied. It achieves the performance with high sensitivity and large measurement range. Here, the sensitivity of the MZI is higher than that of the MI by about two orders in magnitude. Then the MZI composed of large offset splicing single-mode fiber structure is adopted to work for high-sensitivity measurement of salinity. While the MI composed of a photonic crystal fiber (PCF) spliced with two single-mode fiber is adopted to extend the measurement range of the MZI structure. After proper data processing algorithm, it can meet the requirement of salinity measurement in seawater. According to experiments and data processing results, the sensitivity of proposed method could be up to -16602.8 nm/refractive index unit (RIU) in the range of 1.333-1.341, which is corresponding to 3.32056 nm/‰ in the salinity range of 0-40‰ at 25°C. The proposed method could effectively expand the measurement range of high-sensitivity MZI according to the requirement, and also has the potential for multi-parameter measurement.

Detecting momentum weak value: Shack-Hartmann versus a weak measurement wavefront sensor.

The task of wavefront sensing is to measure the phase of the optical field. Here, we demonstrate that the widely used Shack-Hartmann wavefront sensor detects the weak value of transverse momentum, usually achieved by the method of quantum weak measurement. We extend its input states to partially coherent states and compare it with the weak measurement wavefront sensor, which has a higher spatial resolution but a smaller dynamic range. Since weak values are commonly used in investigating fundamental quantum physics and quantum metrology, our work would find essential applications in these fields.

Speech extraction with RGB-intensity gradient on rolling-shutter video

Recent studies have been proposed to extract speech from the captured video of objects vibrating by sound waves. Among them, from the viewpoint of equipment cost, the method of extracting speech from the video captured by rolling-shutter cameras, which are widely used in consumer digital single-lens reflex cameras, has been attracting attention. The conventional method with the rolling-shutter video uses a grayscale video for processing based on phase images. However, a grayscale video has a smaller dynamic range than an RGB video, and thus the speech extraction accuracy of the conventional method degrades. Therefore, this paper proposes a speech extraction method based on RGB-intensity gradients on an RGB video to improve speech extraction accuracy. The proposed method extracts the speech by calculating the similarity of R, G, and B intensity gradients, and using these three intensity gradients expands the dynamic range. The experimental results on the quality and intelligibility of the extracted speech show our proposed method outperforms the conventional method.

Time-resolved fluorescence immunoassay for urine retinol-binding protein is more sensitive than polyclonal and monoclonal assays.

Retinol-binding protein4 (RBP) assays using polyclonal antibodies (pRBP) have major problems of non-linearity of dilution and a very small useable dynamic range. Our objective was to develop a specific assay with a wider dynamic range to detect tubular proteinuria. mRBP (monoclonal capture and second antibody with colorimetric detection) and fluoroimmunoassays for RBP (fRBP) (polyclonal capture and monoclonal second antibody with fluorescence detection) were developed and compared with pRBP. Four hundred and eighty-eight patient samples were collected; 290 samples were analysed by mRBP and 198 samples with fRBP and compared with pRBP. mRBP assay has the advantages of better linearity on dilution and wider analytical range over pRBP. It is limited by poor signal in the patients with albuminuria and glomerular proteinuria and inferior discrimination between patient groups. fRBP had an intra-assay and inter-assay CV of <6% and <8%, respectively, and analytical range was 2.3-599 µg/L. fRBP was linear on dilution within the analytical range. Correlation (r) was 0.8722 (95% CI 0.7621 to 0.9333, P< 0.0001); Mann-Whitney test revealed no significant difference (U = 18,877, n = 198, P = 0.5244) asserting that the medians of the two samples were identical. Bland-Altman test between pRBP and fRBP showed a mean negative bias of 16.43 (CI -994 to 1027) µg/mmol. The combination assay with fluorescence detection (fRBP) proved more discriminatory than a purely monoclonal system especially in patients with significant proteinuria and has advantages of better linearity on dilution and wider analytical range than the existing pRBP assay and compared extremely well with pRBP.

Detection of metallicity correlations in 100 nearby galaxies

ABSTRACT In this paper, we analyse the statistics of the 2D gas-phase oxygen abundance distributions of 100 nearby galaxies drawn from the Calar Alto Legacy Integral Field spectroscopy Area survey. After removing the large-scale radial metallicity gradient, we compute the two-point correlation functions of the resulting metallicity fluctuation maps. We detect correlations in the majority of our targets, which we show are significantly in excess of what is expected due to beam-smearing, and are robust against the choice of metallicity diagnostic. We show that the correlation functions are generally well-fit by the predictions of a simple model for stochastic metal injection coupled with diffusion, and from the model we show that, after accounting for the effects of both beam smearing and noise, the galaxies in our sample have characteristic correlation lengths of ∼1 kpc. Correlation lengths increase with both stellar mass and star formation rate, but show no significant variation with Hubble type, barredness, or merging state. We also find no evidence for a theoretically predicted relationship between gas velocity dispersion and correlation length, though this may be due to the small dynamic range in gas velocity dispersion across our sample. Our results suggest that measurements of 2D metallicity correlation functions can be a powerful tool for studying galaxy evolution.

Modeling a grating coupler-based interferometer for far-field-sensing of nanoscale displacements.

Sensing displacements at the nanoscale is the basis for many metrology applications, in particular atomic-force microscopy. Displacement sensing with nano-optomechanical structures provides interesting opportunities for integration, but it typically features a small dynamic range due to the near-field nature of the sensor-sample interaction. Here, a far-field sensing approach based on a grating coupler is considered and an analytical model used to tune its performance is introduced. The proposed model allows exploiting the full range of design parameters and thereby optimizing resolution and dynamic range. The compact size of the sensor and the possibility of integrating it with an on-chip laser and detector make it very promising for fully-integrated optical sensing systems.

The Oxide Film-Coated Surface Acoustic Wave Resonators for the Measurement of Relative Humidity

The surface acoustic wave (SAW) humidity sensor may possess many attractive sensing characteristics such as high sensitivity, high resolution, high stability, frequency output, ease of interfacing, small size, and broad dynamic range. Mostly, the polymer materials are used for the SAW humidity sensor fabrication. But the polymer SAW sensors suffer from broad bandwidth, instability due to ambient temperature, nonlinearity, and small dynamic range. This article presents the fabrication of metal oxide film humidity sensors using SAW resonators of 433.92-MHz frequency. Five different SAW humidity sensors were fabricated by varying the deposited alumina film thickness to measure humidity in the range of 0%–98% relative humidity (RH). The hydrophilic films were formed by dip coating of alumina solution of different molar concentrations. The alumina film is thermally stable and inert. The static and dynamic response characteristics were determined from the shift in resonant peaks at different humidity using an HP 85046A vector network analyzer (VNA). The minimum sensitivity of the least sensitive sensor was found to be 2.51 kHz/%RH. The sensors show linear response ( $R^{2} \ge0.98$ ), high sensitivity (max. ~ 9 kHz/%RH), negligible hysteresis error (≤0.50%), wide dynamic range, and inexpensive fabrication due to the use of commercial resonators. The response parameters of the sensors were compared with the parameters of other oxide SAW sensors reported in the literature. Finally, the linear sensor was interfaced to the electronic and associated signal conditioning circuits to display humidity in %RH.

A coreless hybrid magnetic coupling coil structure for dynamic wireless power charging system

A coreless hybrid magnetic coupling mechanism has been studied and designed in the dynamic wireless charging system, through the mathematical model building, the parameter analysis and the model simulation comparison. The mechanism can improve the endurance of the electric vehicles, reduce the times of the power supply of the electric vehicles, and increase the offset ability. First of all, the dynamic model of the magnetic coupling mechanism is established based on the Kirchhoff’s law of the voltage and current, and the output power and the system efficiency are deduced considering the driving speed of the electric vehicle. Then, the influence of the mutual inductance between coils on the output power and the system efficiency is deeply analyzed, and the mutual inductance between coils is increased by adding the magnetic potential at a proper position, to propose and analyze a coreless hybrid magnetic coupling coil structure. Finally, the co-simulations are implemented through the finite element analysis simulations to compare and analyze the coil structure performance before and after the optimization. The results show that the designed coil structure can effectively improve the coupling between magnetic fields, and increase the offset ability. The mechanism has the characteristics of the small dynamic output power fluctuation range and high efficiency, and then can realize the stable power supply for the mobile power load.

Readout integrated circuit with multi-mode background suppression for long wavelength infrared focal plane arrays

Due to the large background current and the large dark current of non-uniformity, the readout integrated circuit (ROIC) will have the small dynamic range and the large spatial noise. For currently-used background and dark current suppression (BDS) techniques, reduction of spatial noise and provision of fast frame rates by a single circuit is impractical. In this paper, we propose a novel BDS circuit for use in different situations. With this circuit, three readout modes can be selected based on the magnitudes of the dark current and the background current, dynamic range, heterogeneity, and frame rate, while a double-threshold comparator is used to ensure the accuracy of BDS. To design the BDS module, we measured the dark current of a collection of long wavelength infrared mercury cadmium telluride photovoltaic detectors, to ensure that suppression could be provided for a statistically significant proportion of devices. Noise in the BDS module was analyzed based on BDS accuracy. Finally, the outputs obtained in the three readout modes were compared in simulation, to verify the operation of the BDS module. The BDS circuit can be used as reference for the large scale of an infrared focal plane array.

Fusion of Multi-Satellite Data and Artificial Neural Network for Predicting Total Discharge

As research on the use of satellites in combination with previous hydrological monitoring techniques increases, interest in the application of the machine-learning approach to the prediction of hydrological variables is growing. Ground-based measurements are often limited due to the difficulties in measuring spatiotemporal variations, especially in ungauged areas. In addition, there are no existing satellites capable of measuring total discharge directly. In this study, Artificial neural network (ANN) machine-learning approaches are examined for the prediction of 0.25° total discharge data over the Korean Peninsula using the data fusion of multi-satellites, reanalysis data, and ground-based observations. Terrestrial water storage changes (TWSC) of the Gravity Recovery and Climate Experiment (GRACE) satellite, precipitation of the tropical rainfall measuring mission (TRMM), and soil moisture storage and average temperature of the global land data assimilation system (GLDAS) models are used as ANN model input data. The results demonstrate the relatively good performance of the ANN approach for predicting the total discharge in terms of the correlation coefficient (r = 0.65–0.95), maximum absolute error (MAE = 13.28–20.35 mm/month), root mean square error (RMSE = 22.56–34.77 mm/month), and Nash-Sutcliff efficiency (NSE = 0.42–0.90). The precipitation is identified as the most influential input parameter through a sensitivity analysis. Overall, the ANN-predicted total discharge shows similar spatial patterns to those from other methods, while GLDAS underestimates the total discharge with a smaller dynamic range than the other models. Thus, the potential of the ANN approach described herein shows promise for predicting the total discharge based on the data fusion of multi-satellites, reanalysis data, and ground-based observations.

Tunneling conductance of long-range Coulomb interacting Luttinger liquid

The theoretical model of the short-range interacting Luttinger liquid predicts a power-law scaling of the density of states and the momentum distribution function around the Fermi surface, which can be readily tested through tunneling experiments. However, some physical systems have long-range interaction, most notably the Coulomb interaction, leading to significantly different behaviors from the short-range interacting system. In this paper, we revisit the tunneling theory for the one-dimensional electrons interacting via the long-range Coulomb force. We show that even though in a small dynamic range of temperature and bias voltage, the tunneling conductance may appear to have a power-law decay similar to short-range interacting systems, the effective exponent is scale-dependent and slowly increases with decreasing energy. This factor may lead to the sample-to-sample variation in the measured tunneling exponents. We also discuss the crossover to a free Fermi gas at high energy and the effect of the finite size. Our work demonstrates that experimental tunneling measurements in one-dimensional electron systems should be interpreted with great caution when the system is a Coulomb Luttinger liquid.

Collapse of Global Neuronal States in Caenorhabditis elegans under Isoflurane Anesthesia.

A comprehensive understanding of how anesthetics facilitate a reversible collapse of system-wide neuronal function requires measurement of neuronal activity with single-cell resolution. Multineuron recording was performed in Caenorhabditis elegans to measure neuronal activity at varying depths of anesthesia. The authors hypothesized that anesthesia is characterized by dyssynchrony between neurons resulting in a collapse of organized system states. Using light-sheet microscopy and transgenic expression of the calcium-sensitive fluorophore GCaMP6s, a majority of neurons (n = 120) in the C. elegans head were simultaneously imaged in vivo and neuronal activity was measured. Neural activity and system-wide dynamics were compared in 10 animals, progressively dosed at 0%, 4%, and 8% isoflurane. System-wide neuronal activity was analyzed using principal component analysis. Unanesthetized animals display distinct global neuronal states that are reflected in a high degree of correlation (R = 0.196 ± 0.070) between neurons and low-frequency, large-amplitude neuronal dynamics. At 4% isoflurane, the average correlation between neurons is significantly diminished (R = 0.026 ± 0.010; P < 0.0001 vs. unanesthetized) and neuron dynamics shift toward higher frequencies but with smaller dynamic range. At 8% isoflurane, interneuronal correlations indicate that neuronal activity remains uncoordinated (R = 0.053 ± 0.029; P < 0.0001 vs. unanesthetized) with high-frequency dynamics that are even further restricted. Principal component analysis of unanesthetized neuronal activity reveals distinct structure corresponding to known behavioral states. At 4% and 8% isoflurane this structure is lost and replaced with randomized dynamics, as quantified by the percentage of total ensemble variance captured by the first three principal components. In unanesthetized worms, this captured variance is high (88.9 ± 5.4%), reflecting a highly organized system, falling significantly at 4% and 8% isoflurane (57.9 ± 11.2%, P < 0.0001 vs. unanesthetized, and 76.0 ± 7.9%, P < 0.001 vs. unanesthetized, respectively) and corresponding to increased randomization and collapse of system-wide organization. Anesthesia with isoflurane in C. elegans corresponds to high-frequency randomization of individual neuron activity, loss of coordination between neurons, and a collapse of system-wide functional organization.

Dynamic Range Expansion Method of Three-component HTc SQUIDs Magnetometer

In view of the small dynamic range of the three-component high-temperature superconducting quantum interference device (HTc SQUID) magnetometer and it is easy to exceed the measurement range when working in the field without magnetic shielding environment. A dynamic range expansion method based on step compensation is proposed. By setting a magnetic compensation coil on each axis of the three-component HTc SQUIDs magnetometer probe, a current is passed to generate a magnetic field opposite to the direction of the external magnetic field to offset most of the magnetic field to be measured. The measured values are calculated and processed to obtain the magnetic field value to be measured. The test results show that the method can greatly improve the dynamic range without reducing the sensitivity of the magnetometer, which can work stably in a non-magnetic shielding environment and meet the requirements of field geophysical magnetic exploration.

Near threshold voltage digital PLL using low voltage optimised blocks for AR display system

In this work, a digital phase-locked loop (DPLL) is proposed for the low power and stable operation in an augmented reality (AR) display system. The AR display system which needs the ultra-low power consumption adopts the near-threshold voltage (NTV) operation in both digital and analogue blocks. The NTV region has the advantage of small power dissipation with low-supply voltage. However, it suffers from performance degradation due to a large delay, slow transition time, and small dynamic voltage range. To achieve the optimised power efficiency and stable performance, the dynamic time-to-digital converter and the low voltage optimised digitally controlled oscillator are applied in the proposed DPLL. In addition, the forward body biasing scheme is used to increase the operation frequency for the sigma-delta modulator block. The proposed DPLL is fabricated using 65 nm CMOS technology and shows a current consumption of 160 μA at a voltage of 0.55 V. In addition, the jitter characteristic shows 6.7 ps rms jitter and 50 ps peak to peak jitter at 480 MHz.

Improvement of analogue switching characteristics of MoS2 memristors through plasma treatment

Memristive devices based on 2D materials, such as WSe2 and MoS2, have been demonstrated to exhibit analogue switching characteristics and enable emulation of ionic interactions involved in synaptic activities. These attractive features hold great potential for construction of energy-efficient artificial neural networks. However, the memristors made from pristine 2D materials typically exhibit a small dynamic range and poor linearity of switching characteristics. The neural network simulated in the basis of such switching characteristics has a poor learning accuracy of ~43%. In this work, we find that Ar plasma treatment can greatly improve both the dynamic range and linearity of analogue switching characteristics of few-layer MoS2 memristors. The neural network consisting of such plasma-treated memristors is simulated to be able to result in a significantly improved learning accuracy of 94.3% for the MNIST handwritten digits dataset. Our additional Auger electron analysis in combination with electronic characterizations indicates that Ar plasma enhances the concentration of movable S vacancies in MoS2 channels, which leads to improvements in the analogue switching properties. Especially, the average dynamic range of MoS2 memristors are increased from 1.8 to 14.8 after plasma treatment. This work provides scientific insights for controlling the switching characteristics of 2D memristors and provides technical instruction for construction of practical neural networks based on 2D materials.

Power Gain Pattern Synthesis via Successive Convex Approximation Technique

Wide beam is necessary for ensuring the main lobe direction in a mobile communication scenario. A small dynamic range ratio (DRR) of excitations is crucial for simple-array and energy-saving design. The power gain pattern synthesis (PGPS) problem is aimed to maximize the minimum power gain in the wide main lobe and solving the PGPS problem can form a wide beam. To control the DRR, an upper bound constraint of DRR is imposed on the PGPS problem. The new problem is concave and can't be solved effectively by the conventional convex optimization method. We convert the DRR constraint into a group of stricter inequalities and transform the concave constraints to be convex with some convex approximations. The general PGPS problem with DRR constraints can be solved by the successive convex approximation (SCA) technique. The weights obtained by the proposed method can converge to the stationary point and we present the convergence proofs. Simulation results show that the proposed algorithm has better performance on increasing the minimum power gain in the main lobe (PGML) and suppressing the sidelobe level (SLL). Meanwhile, the DRR of excitations can be controlled below a given upper bound.

Novel Design of a Layered Electrochemical Dopamine Sensor in Real Samples Based on Gold Nanoparticles/β-Cyclodextrin/Nafion-Modified Gold Electrode.

Change in the level of dopamine (DA) concentration in the human body causes critical diseases such as schizophrenia and Parkinson’s disease. Therefore, the determination of DA concentration and monitoring its level in human body fluids is of great importance. An electrochemical sensor based on modification of the gold electrode surface with Nafion (NF), β-cyclodextrin (CD), and gold nanoparticles (AuNPs) was fabricated for the determination of DA in biological fluids. Combined impact of all the modifiers enhances the catalytic activity of the sensor. Gold nanoparticles increase the surface area of the sensor and enhance the electron transfer rate. CD plays a main role in enhancing the accumulation of protonated DA and forming stable complexes via electrostatic interactions and hydrogen bond formation. In addition, extra preconcentration of positively charged DA is achieved through ionic selectivity of NF. High electrocatalytic activity was achieved using the modified sensor for determination of DA in real urine samples in a wide concentration range, 0.05–280 μM with a low detection limit of 0.6 nM in the small linear dynamic range, 0.05–20 μM. Furthermore, common overlapped oxidation peaks of DA in presence of biologically interfering compounds at the gold electrode were resolved by using the modified sensor. Excellent recovery results were obtained using the proposed method for determination of DA in real urine samples.

A tight cold-inducible switch built by coupling thermosensitive transcriptional and proteolytic regulatory parts.

Natural organisms have evolved intricate regulatory mechanisms that sense and respond to fluctuating environmental temperatures in a heat- or cold-inducible fashion. Unlike dominant heat-inducible switches, very few cold-inducible genetic switches are available in either natural or engineered systems. Moreover, the available cold-inducible switches still have many shortcomings, including high leaky gene expression, small dynamic range (<10-fold) or broad transition temperature (>10°C). To address these problems, a high-performance cold-inducible switch that can tightly control target gene expression is highly desired. Here, we introduce a tight and fast cold-inducible switch that couples two evolved thermosensitive variants, TFts and TEVts, as well as an additional Mycoplasma florum Lon protease (mf-Lon) to effectively turn-off target gene expression via transcriptional and proteolytic mechanisms. We validated the function of the switch in different culture media and various Escherichia coli strains and demonstrated its tightness by regulating two morphogenetic bacterial genes and expressing three heat-unstable recombinant proteins, respectively. Moreover, the additional protease module enabled the cold-inducible switch to actively remove the pre-existing proteins in slow-growing cells. This work establishes a high-performance cold-inducible system for tight and fast control of gene expression which has great potential for basic research, as well as industrial and biomedical applications.

Efficient Modular Adder Designs Based on Thermometer and One-Hot Coding

Residue number systems (RNSs) are efficient alternatives to positional number systems, providing fast and power-efficient computational systems. The key feature of the RNS benefitting modern embedded systems and the Internet-of-Thing (IoT) edge devices is its energy efficiency. Modular addition is the most important and frequent operation applied on the components of RNS, including arithmetic units in the channels as well as forward and reverse converters. The small and medium dynamic range requirements of low-power embedded and edge devices make the usage of the thermometer coding (TC) and one-hot coding (OHC) viable, reducing the power consumption and improving the energy efficiency of modulo addition in comparison to regular binary representations. Based on these techniques, this paper presents two new energy-efficient modular adders, which, due to the carry-free internal computations, are also highly performing. The proposed modular adders based on the TC and OHC result in average improvements of 38% and 34.5% for the delay, 27% and 14.5% for the circuit area, 29.5% and 6.3% for energy consumption, and about 54.9% and 44.2% for the area-delay product (ADP), respectively, in comparison with the related state of the art.

Fine-Scale SAR Soil Moisture Estimation in the Subarctic Tundra

In the subarctic tundra, soil moisture information can benefit permafrost monitoring and ecological studies, but fine-scale remote-sensing approaches are lacking. We explore the suitability of C-band SAR, paying attention to two challenges soil moisture retrieval faces. First, the microtopography and the heterogeneous organic soils impart unique microwave scattering properties, even in absence of noteworthy shrub cover. Empirically, we find the polarimetric response is highly random (entropies >0.7). The randomness limits the applicability of purely polarimetric approaches to soil moisture estimation, as it causes a tailor-made decomposition to break down. For comparison, the L-band scattering response is more surfacelike, also in terms of its angular characteristics. The second challenge concerns the large spatial but small temporal variability of soil moisture observed at our site. Accordingly, the Radarsat-2 C-band backscatter has a limited dynamic range (~2 dB). However, contrary to polarimetric indicators, it shows a clear surface soil moisture signal. To account for the small dynamic range while retaining a 100-m spatial resolution, we embed an empirical time-series model in a Bayesian framework. This framework adaptively pools information from neighboring grid cells, thus increasing the precision. The retrieved soil moisture index achieves correlations of 0.3–0.5 with in situ data at 5 cm depth and, upon calibration, root-mean-square errors of <0.04 m3m−3. As this approach is applicable to Sentinel-1 data, it can potentially provide frequent soil moisture estimates across large regions. In the long term, L-band data hold greater promise for operational retrievals.