Coarse Steps Research Articles

Treatment of anaerobically digested pig manure by applying membrane processes for nutrient recovery and antibiotics removal.

A combination of membrane processes was applied to treat the digestate produced after the anaerobic treatment of pig manure in a biogas plant, aiming towards the recovery of nutrients and effective water treatment for potential reuse. Initially, coarse filtration (sieving and microfiltration) was used to remove particles larger than 1 µm, followed by ultrafiltration, to reduce the suspended solids concentrations below 1g/L. Subsequently, selective electrodialysis is employed to recover the main nutrient ions, primarily ammonium and potassium. The ion-depleted digestate is then fed to a reverse osmosis unit, where clean water was recovered, yielding a by-product (concentrate) stream enriched in phosphates and organics content. The presence of antibiotics and the concentrations of heavy metals were monitored during all treatment stages to assess their behavior/removal in the various membrane processes. The results indicate that almost 51% of the digestate could be recovered as water free from ions and antibiotics, suitable for reuse in the biogas plant for process needs and irrigation purposes. The selective electrodialysis process can recover 51% of initial NH4+ content (corresponding to 96% of the electrodialysis feed), while the remainder largely ended up in the ultrafiltration concentrate. A similar behavior was observed for the case of K+, while approximately 68% of the phosphates content was retained by the coarse filtration process, with another 24% remaining in the ultrafiltration concentrate and the remaining 8% in the reverse osmosis concentrate. Most of the antibiotics and heavy metals were retained by the coarse and ultrafiltration steps, with smaller amounts detected in the reverse osmosis concentrate.

SAR Image Reconstruction via Incremental Imaging With Compressive Sensing

For synthetic aperture radar (SAR) imaging, the irregular loss of received data and the non-uniformly under sampling yield the SAR azimuth ambiguity (SAA) resulting in the degradation in image quality. To address this issue, the incremental SAR imaging approach based on the estimation of sensing dictionary matrix in the pursuit of sparsity is presented in this paper. Several beneficial contributions are included in the proposed method. First, the SAA reduction achievable in the proposed method is considerably improved more than that of the conventional compressive sensing (CS) based approach in terms of the image quality and computational efficiency. Second, we established the signal parameterization scheme which is divided into coarse and fine search steps to estimate the sensing matrix for SAR image restoration via signal model reconstruction. Third, an incremental imaging approach is devised to overcome the drawback of the conventional CS-based approach which is not sufficiently good leading to limited SAA reduction performance under the non-sparse SAR image. These contributions are verified using numerical simulations and experimental results.

Squeezed states generation by nonlinear plasmonic waveguides: a novel analysis including loss, phase mismatch and source depletion

In this article, a full numerical method to study the squeezing procedure through second harmonic generation process is proposed. The method includes complex nonlinear coupling coefficient, phase mismatch, and pump depletion. Attention has been also paid to the effects of accumulated noises in this work. The final form of the numerical formula seems to be much simpler than the analytical solutions previously reported. The function of this numerical method shows that it works accurately for different mechanisms of squeezed state generations and does not suffer from instabilities usually encountered even for non-uniform, coarse steps. The proposed method is used to examine the squeezing procedure in an engineered nonlinear plasmonic waveguide. The results show that using the nonlinear plasmonic waveguide, it is possible to generate the squeezed states for the pump and the second harmonic modes with high efficiency in a propagation length as short as 2 mm which is much shorter than the needed length for the traditional nonlinear lithium niobate- based optical waveguides being of the order of 100 mm. This new method of squeezed states generation may find applications in optical communication with a noise level well below the standard quantum limit, in quantum teleportation, and in super sensitive interferometry.

Reconfigurable Liquid Metal-Based SIW Phase Shifter

This article presents the first substrate integrated waveguide (SIW) phase shifter that can be reconfigured using liquid metal (LM). This digital phase shifter exhibits low insertion loss and is reciprocal and bidirectional. It incorporates a series of holes which can be filled or emptied of liquid metal, enabling us to add or remove via connections dynamically, on-the-fly. Using a collection of such holes, it is possible to create a wall along the E-plane or H-plane of the waveguide. When the wall is in place, it blocks the passage of energy. When the wall is absent, energy is able to flow. In this way, it is possible to guide the electromagnetic (EM) waves through one of three paths, having different electrical lengths. The result is a digital switched-line phase shifter that achieves coarse steps of phase change, from 0° up to 180°, in steps of 60°. By filling or emptying individual holes, it is possible to introduce reactive loading into each path. In this way, it is possible to achieve fine phase control in steps of 10°. Using both forms of reconfiguration in unison, the proposed phase shifter is able to deliver a phase shift of up to 180°, in steps of 10°. The proposed phase shifter operates at 10 GHz and exhibits an insertion loss of less than 2.3 dB over its entire operating band. Furthermore, the underlying concept of the proposed phase shifter can be readily scaled for operation in the millimeter-wave (mm-wave) band. The existing phase shifters operating in that band exhibit significant insertion losses. MM-wave phase shifters are expected to find application in 5G mobile access points.

Multiscale data assimilation in the Bluelink ocean reanalysis (BRAN)

Forecast errors of subsurface temperature and salinity are substantially reduced with an efficient, two-step, multiscale Ensemble Optimal Interpolation (EnOI) system, applied to a near-global eddy-resolving ocean model. A critical element of any data assimilation system is the background error covariance, which for EnOI is typically a static ensemble of anomalies from a long model run. Here, we construct two ensembles — one based on intraseasonal anomalies from a free run of the same eddy-resolving ocean model used to underpin the forecasts, and a second ensemble of climatogical anomalies calculated using a relatively coarse, 1-degree global ocean model. For each assimilation cycle, the coarse-resolution ensemble is used to “correct” the broad-scales, and the high-resolution ensemble is used to “correct” the eddy-scales. Corrections from the coarse steps are more effective at reducing systematic errors in the subsurface ocean whereas the high-resolution steps typically produce vertically coherent corrections associated with mesoscale eddies. We compare two configurations of multiscale data assimilation with different localisation radii in the coarse data assimilation step. The best performance and slowest error growth was found with localisation that was large enough to encompass neighbouring profiles in each assimilation cycle. The efficacy of the approach is demonstrated in ocean reanalyses over 2017-8 that assimilate data every 3 days. We demonstrate clear improvements in the representation of temperature and salinity at all depths around Australia. Model-observation differences are particularly improved in and below the thermocline. The corrections to the ocean state with multiscale data assimilation follow water mass structures. The increased computational cost of this multiscale approach is modest (about double the analysis step), but the performance improvement is significant, making this approach suitable for research and operational applications.

Three-Step Cyclic Vernier TDC Using a Pulse-Shrinking Inverter-Assisted Residue Quantizer for Low-Complexity Resolution Enhancement

Herein, we present a cyclic Vernier time-to-digital converter (TDC) using a pulse-shrinking inverter-assisted residue quantizer (IRQ). Previous pulse-shrinking techniques suffer from a nonuniform shrink rate and time offset that slows the conversion and increases power consumption. The proposed pulse-shrinking IRQ reduces the time difference between residue signals instead of shrinking the pulse width. This approach achieves a high resolution with low power consumption and a high conversion rate. The critical tradeoff between resolution and dynamic range (DR) is addressed using a three-step cyclic conversion approach: coarse, fine, and residue quantization steps. The adverse effect of the oscillator startup time on linearity is analyzed, and a correction method is proposed using both on-chip circuit design and off-chip data processing. The proposed TDC is fabricated using a 180-nm CMOS process in a core area of 0.11 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The TDC can be configured into one of four resolution modes, 36, 64, 89, and 135 ps, at conversion rates of 0.82, 1.29, 1.47, and 1.86 MS/s, respectively. A wide DR of up to 179 ns is achieved. The linearity is well performed with a maximum integral nonlinearity (INL)/differential nonlinearity (DNL) of 0.4/0.73 LSB at a conversion rate of 1.86 MS/s. An effective number of bits, N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linear</sub> up to 9.88-bit, and a figure-of-merit (FoM) down to 0.31 pJ/conversion are achieved by consuming 0.55 mW.

Automatic Target Recognition Based on Alignments of Three-Dimensional Interferometric ISAR Images and CAD Models

Inverse synthetic aperture radar (ISAR) is capable of producing 2-D and 3-D images of non-cooperative targets. Compared with 2-D ISAR images, 3-D ISAR reconstructions can provide not only range and cross-range information, but also the information about the third dimension of the target, which is of great significance for automatic target recognition (ATR) and even more specifically to the case of non-cooperative target recognition. The alignment between the point-like 3-D ISAR reconstructions of targets and the targets’ models, such as computer aided design (CAD) models, becomes one of the essential issues in ATR that uses 3-D ISAR reconstructions. In this article, we propose an approach to address the alignment problem. The alignment problem can be decomposed into two steps, a coarse alignment and an accurate alignment. The coarse alignment can be accomplished by means of the principal component analysis (PCA), whereas the accurate alignment can be achieved by iterative closest point algorithm. In this article, the 180° ambiguity issue in PCA is discussed, and a corresponding robust solution is proposed. Moreover, the k-d tree is utilized to accelerate the searching for the pairs of correspondences, both in coarse and accurate alignment steps. In order to simulate real radar scenarios, target self-occlusion is also taken into consideration. The experimental results based on target's scattering point model simulation as well as the electromagnetic simulation in different radar scenarios verify the validity of the proposed method.

Parallel-in-Time Simulation Algorithm for Power Electronics: MMC-HVdc System

The complexity in simulating power electronics, such as modular multilevel converters (MMCs), requires simulation algorithms to speed up the process. Existing simulation algorithms exploit spatial parallelism to speed up the simulation. With the rise in the complexity of power electronics and the presence of an increased number of states within them, there are limits in the speedup using spatial parallelism. In this article, a temporal parallelism algorithm based on parallel-in-time methods is developed for simulation of power electronics systems. The temporal parallelism algorithm is based on the computation of power electronics states on coarse and fine time steps using different models. The models of power electronics systems used in coarse and fine time steps are average-value and detailed models, respectively. The updates to states on coarse time step are computed serially and are used to initialize the states on the fine time step. The updates on fine time step are computed in parallel. A translation method is proposed in this article to update the states on fine time step from the simulations in the coarse time step, and vice versa. The proposed algorithm is applied to simulate MMCs and is validated with respect to a detailed reference MMC model. The proposed algorithm recorded a speedup of up to $3.47 \times $ with five cores.

Moiré-Based Alignment Using Centrosymmetric Grating Marks for High-Precision Wafer Bonding.

High-precision aligned wafer bonding is essential to heterogeneous integration, with the device dimension reduced continuously. To get the alignment more accurately and conveniently, we propose a moiré-based alignment method using centrosymmetric grating marks. This method enables both coarse and fine alignment steps without requiring additional conventional cross-and-box alignment marks. Combined with an aligned wafer bonding system, alignment accuracy better than 300 nm (3σ) was achieved after bonding. Furthermore, the working principle of the moiré-based alignment for the backside alignment system was proposed to overcome the difficulty in bonding of opaque wafers. We believe this higher alignment accuracy is feasible to satisfy more demanding requirements in wafer-level stacking technologies.

A Varactor-Less DCO With $7~GHz$ Tuning Range for $77~GHz$ Automotive Radars

The generation of precise linear frequency modulation is a critical requirement for millimeterwave automotive radars. This paper presents the analysis and design of a CMOS 75.5 - 82.5 GHz monotonically linear digitally controlled oscillator (ML-DCO) fulfilling the requirements of the 77 GHz automotive radar. Non-linear large varactors are avoided. Linear coarse, intermediate, and fine-tuning ranges are achieved by arranging the available minimum Metal-Oxide-Metal (MoM) capacitor in unique configurations. Moreover, a new tuning mechanism derived from a Colpitts topology is discussed to meet the wide tuning requirements without losing linearity. The new arrangement results in a minimal amplitude variation while the ML-DCO is tuned. The coarse, intermediate, and fine-tuning steps from the post-layout simulation are 0.22, 10, and 346 KHz, respectively. The power consumption is 14 mW from a 1.2 V supply. The achieved phase-noise is -98.53 dBc/Hz at a 1 MHz offset when oscillating at 77 GHz. The ML-DCO demonstrates -185 Figure of Merit and -184 Figure of Merit for Tuning.

A Nanosecond-Transient Fine-Grained Digital LDO With Multi-Step Switching Scheme and Asynchronous Adaptive Pipeline Control

This paper introduces a multi-step switching scheme for a digital low dropout regulator (DLDO) that emerges as a new way of achieving nanosecond-transient and fine-grained on-chip voltage regulation. The multi-step switching scheme takes advantage of the adaptive pipeline control and asynchronous clocking for area- and power-efficient digital controller utilization. It speeds up the transient response by varying the pass transistor sizing in two available lengths of coarse steps as per the perturbation, while maintaining a small output voltage ripple by toggling in a finer step at steady operation. A prototype proving the proposed concept, i.e., a 0.6–1.0-V input, 50–200-mV dropout, and 500-mA maximum loading DLDO with an on-chip 1.5-nF output capacitor, is fabricated in a 65-nm CMOS process to verify the effectiveness of this scheme. By employing the multi-step switching scheme and adaptive control, the DLDO achieved a fast transient response to nanoseconds loading current change, and a 100 mV per 10-ns reference voltage switching, as well as a resolution of 768 levels (~9.5 bits) with a 5-mV output ripple. The quiescent current consumed by this DLDO at steady operation is down to $300~{\mu }\text{A}$ .

A 0.04 mm (2) Buck-Boost DC-DC Converter for Biomedical Implants Using Adaptive Gain and Discrete Frequency Scaling Control.

This paper presents the design of a reconfigurable buck-boost switched-capacitor DC-DC converter suitable for use in a wide range of biomedical implants. The proposed converter has an extremely small footprint and uses a novel control method that allows coarse and fine control of the output voltage. The converter uses adaptive gain control, discrete frequency scaling and pulse-skipping schemes to regulate the power delivered to a range of output voltages and loads. Adaptive gain control is used to implement variable switching gain ratios from a reconfigurable power stage and thereby make coarse steps in output voltage. A discrete frequency scaling controller makes discrete changes in switching frequency to vary the power delivered to the load and perform fine tuning when the output voltage is within 10% of the target output voltage. The control architecture is predominately digital and it has been implemented as part of a fully-integrated switched-capacitor converter design using a standard bulk CMOS 0.18 μm process. Measured results show that the converter has an output voltage range of 1.0 to 2.2 V, can deliver up to 7.5 mW of load power and efficiency up to 75% using an active area of only 0.04 mm (2), which is significantly smaller than that of other designs. This low-area, low-complexity reconfigurable power converter can support low-power circuits in biomedical implant applications.

High Frame-Rate VGA CMOS Image Sensor Using Non-Memory Capacitor Two-Step Single-Slope ADCs

This paper proposes a column-parallel two-step single-slope analog-to-digital converter (SS ADC) for high-frame-rate CMOS image sensors. The proposed two-step SS ADC circuit does not utilize an analog memory capacitor to store the value of the first ramp step. Instead, to handle problems such as the slope errors of the second ramp and the stored charge error from charge feed-through, it utilizes a very simple digital column circuit consisting of a coarse counter (coarse step counter) and a 4-to-16 decoder. The second ramp (fine ramp) slope has only one slope generator, regardless of the results of the first ramp decisions, to eliminate the slope mismatch between fine ramp slopes. A prototype sensor comprising 640 $\times$ 480 pixels was fabricated with a 0.13- $\mu{\rm m}$ CMOS process. The results of experiments conducted indicate that the proposed ADC can achieve a conversion time of 6.4 $\mu{\rm s}$ at a main clock frequency of 62.5 MHz, which is 10.2 times faster than the conventional SS ADC. The maximum frame rate of the proposed VGA CMOS Image Sensor (CIS) is 320 frames per second (fps). Further, the proposed circuit employs redundancy error correction logic to calibrate the error between the coarse and fine steps. The total power consumption is 72 mW from supply voltages of 2.8 V (analog) and 1.5 V (digital). The figure of merit (FoM) of the proposed VGA CMOS image sensor is 2.01 $[e^{-} {\rm nJ}]$ .

A regional neural network ensemble for predicting mean daily river water temperature

Summary Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate and land use changes, thereby providing information that is valuable to management of river ecosystems and biota such as brook trout.

Mechanical and Tribological Characterization of Al-Mg2Si Composites After Yttrium Addition and Heat Treatment

In this study, the effect of heat treatment and yttrium additions on the microstructure, mechanical properties, and tribological behavior of Al-15% Mg2Si cast composites was investigated. The microstructural study revealed the presence of both primary and secondary Mg2Si phases in all composite specimens and also Y-containing intermetallics (Al2Y phases) at higher concentrations. It was also found that Y addition does not change the size and morphology of primary Mg2Si particles considerably, but the pseudo-eutectic Mg2Si changed from a flake-like morphology to fine fibrous or rod-like one. The results show that proper content of Y additions can reduce the amount of Mg2Si phase through dissolving it into the matrix, lead to the precipitation of Al2Y phase and improve the mechanical properties. Modified composites with 0.5% Y exhibited an ultimate tensile strength (UTS) of 290 MPa with an elongation of 4.3%. After exposing the composite to solution treatment at 520 °C for 4 h, the tensile strength of the composite continuously increased with the increase of Y content, and reached the maximum at 1% Y. The maximum UTS and elongation at room temperature for the heat-treated composites are 294 MPa and 7.4%, respectively. In the cast specimen, fracture surfaces are covered by packets with coarse steps, suggesting a brittle mode of failure. Modified composites with 0.5 wt.% Y contain several cracked particles together with a few decohered primary Mg2Si particles. In solution heat-treated state, dimples present at the fracture surface are rather coarse but homogenous, showing a semi-ductile mode of fracture. Wear test results showed that the wear resistance of all specimens increases with the addition of Y up to 0.3 wt.%. Scanning electron microscopic observations of the worn surfaces revealed that the dominant wear mechanism was abrasive wear accompanied by some delamination wear mode.

Optimal design of 6-DOF eclipse mechanism based on task-oriented workspace

SUMMARYWe present a new numerical optimal design for a redundant parallel manipulator, the eclipse, which has a geometrically symmetric workspace shape. We simultaneously consider the structural mass and design efficiency as objective functions to maximize the mass reduction and minimize the loss of design efficiency. The task-oriented workspace (TOW) and its partial workspace (PW) are considered in efficiently obtaining an optimal design by excluding useless orientations of the end-effector and by including just one cross-sectional area of the TOW. The proposed numerical procedure is composed of coarse and fine search steps. In the coarse search step, we find the feasible parameter regions (FPR) in which the set of parameters only satisfy the marginal constraints. In the fine search step, we consider the multiobjective function in the FPR to find the optimal set of parameters. In this step, fine search will be kept until it reaches the optimal set of parameters that minimize the proposed objective functions by continuously updating the PW in every iteration. By applying the proposed approach to an eclipse-rapid prototyping machine, the structural mass of the machine can be reduced by 8.79% while the design efficiency is increased by 6.2%. This can be physically interpreted as a mass reduction of 49 kg (the initial structural mass was 554.7 kg) and a loss of 496 mm3/mm in the workspace volume per unit length. The proposed optimal design procedure could be applied to other serial or parallel mechanism platforms that have geometrically symmetric workspace shapes.

Deformable model with surface registration for hippocampal shape deformity analysis in schizophrenia

Changes to the hippocampal structure have been reported as consistent structural abnormalities in schizophrenic patients and have been related to the learning and memory deficits in such patients. Although many magnetic resonance (MR) imaging studies have focused on the hippocampal volume, local structural changes were difficult to discriminate from normal neuroanatomical variations. 3D shape deformation analysis of the brain structure may reflect localized schizophrenic abnormalities. A deformable model, evolved from the ellipsoid to hippocampal surface, with 2562 vertexes, was developed to analyze the left and right hippocampus shapes in 22 schizophrenic patients and 22 healthy age and gender matched controls. One of the most critical issues in the shape analysis is the determination of homologous points between two objects. To determine more accurate corresponding points, an alignment procedure, consisting of coarse and fine steps, following a deformation process, was applied. The performance of the alignment process was tested using artificial data, to get the alignment error to within 3° for each angle. A volume analysis indicated the hippocampal volume to be bilaterally reduced in schizophrenic patients compared to the normal controls, with a shape analysis showing a deformity pattern of the hippocampal surface. Bilateral inward deformities in the anterior and posterior hippocampus and a unilateral outward deformity in the right anterior hippocampus were observed, respectively.

Deformable model with surface registration for hippocampal shape deformity analysis in schizophrenia

Changes to the hippocampal structure have been reported as consistent structural abnormalities in schizophrenic patients and have been related to the learning and memory deficits in such patients. Although many magnetic resonance (MR) imaging studies have focused on the hippocampal volume, local structural changes were difficult to discriminate from normal neuroanatomical variations. 3D shape deformation analysis of the brain structure may reflect localized schizophrenic abnormalities. A deformable model, evolved from the ellipsoid to hippocampal surface, with 2562 vertexes, was developed to analyze the left and right hippocampus shapes in 22 schizophrenic patients and 22 healthy age and gender matched controls. One of the most critical issues in the shape analysis is the determination of homologous points between two objects. To determine more accurate corresponding points, an alignment procedure, consisting of coarse and fine steps, following a deformation process, was applied. The performance of the alignment process was tested using artificial data, to get the alignment error to within 3° for each angle. A volume analysis indicated the hippocampal volume to be bilaterally reduced in schizophrenic patients compared to the normal controls, with a shape analysis showing a deformity pattern of the hippocampal surface. Bilateral inward deformities in the anterior and posterior hippocampus and a unilateral outward deformity in the right anterior hippocampus were observed, respectively.

The Filling of Gaps in Geophysical Time Series by Artificial Neural Networks

Nowadays, there is a large number of time series of natural data to study geophysical and astrophysical phenomena and their characteristics. However, short length and data gaps pose a substantial problem for obtaining results on properties of the underlying physical phenomena with existing algorithms. Using only an equidistant subset of the data with coarse steps leads to loss of information. We present a method to recover missing data in time series. The approach is based on modeling the time series with manifolds of small dimension, and it is implemented with the help of neural networks. We applied this approach to real data on cosmogenic isotopes, demonstrating that it could successfully repair gaps where data was purposely left out. Multi-fractal analysis was applied to a true radiocarbon time series after recovering missing data.

Coarse-to-fine planar object identification using invariant curve features and B-spline modeling

This paper presents a hybrid algorithm for coarse-to-fine matching of affine-invariant object features and B-spline object curves, and simultaneous estimation of transformation parameters. For coarse-matching, two dissimilar measures are exploited by using the significant corners of object boundaries to remove candidate objects with large dissimilarity to a target object. For fine-matching, a robust point interpolation approach and a simple gradient-based algorithm are applied to B-spline object curves under MMSE criterion. The combination of coarse and fine-matching steps reduces the computational cost without degrading the matching accuracy. The proposed algorithm is evaluated using affine transformed objects.