Inherent Errors Research Articles

Evaluation of a Planar Reconfigurable Phased Array Antenna Driven by a Multi-Channel Beamforming Module at Ka Band

In this paper, a planar active phased array antenna demonstration with linear polarization (LP) at Ka Band (28-30 GHz) is presented. The proof of concept is carried out to evaluate the possible problems that may arise, to analyze possible calibration stages and to assess the viability of the integration of an active system with a Multi-Channel Beamforming Module (MCBM). To fulfill this task an $8\times 8$ -element planar array arranged in column subarrays of $1\times 8$ elements for 1D beam steering is proposed. The single element consists of a printed circular patch connected to a microstrip feeding line through metallic vias in a multilayered structure. Both the amplitude and phase distributions are performed by a commercial integrated circuit (IC) designed for transmission purposes, from the common port to each of the 8 output ports. Thus, an evaluation of the IC performance is also included within this work. Despite the inherent amplitude and phase feeding errors of the IC, the beam-steering accuracy of the system is reasonable. A nice correspondence between the simulated and measured $8\times 8$ -element array beam steering directions is obtained, with errors below 1° in the steering of the beam.

Longitudinal Impact of Preference Biases on Recommender Systems' Performance

Research studies have shown that recommender systems' predictions that are observed by users can cause biases in users' post-consumption preference ratings. Because users' preference ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the performance of the system in the long run. We use a simulation approach to study the longitudinal impact of preference biases (and their magnitude) on the dynamics of recommender systems' performance. We look at the influence of preference biases in two conditions: (i) during the normal system use, where biases are typically caused by the system's inherent prediction errors, and (ii) in the presence of external (deliberate) recommendation perturbations. Our simulation results show that preference biases significantly impair the system's prediction performance (i.e., prediction accuracy) as well as users' consumption outcomes (i.e., consumption relevance and diversity) over time. The impact is non-linear to the size of the bias, i.e., large bias causes disproportionately large negative effects. Also, items that are less popular and less distinctive (in terms of their content) are affected more by preference biases. Additionally, intentional recommendation perturbations, even on a small number of items for a short time, substantially amplify the negative impact of preference bias on a system's longitudinal dynamics and causes long-lasting effects on users' consumption. Our findings provide important implications for the design of recommender systems.

High-cadence optical transient searches using drift scan imaging III: Development of an inexpensive drive control system and characterisation and correction of drive system periodic errors

Abstract In order to further develop and implement novel drift scan imaging experiments to undertake wide-field, high time resolution surveys for millisecond optical transients, an appropriate telescope drive system is required. This paper describes the development of a simple and inexpensive hardware and software system to monitor, characterise, and correct the primary category of telescope drive errors and periodic errors due to imperfections in the drive and gear chain. A model for the periodic errors is generated from direct measurements of the telescope drive shaft rotation, verified by comparison to astronomical measurements of the periodic errors. The predictive model is generated and applied in real time in the form of corrections to the drive rate. A demonstration of the system shows that inherent periodic errors of peak-to-peak amplitude ${\sim}{100}''$ are reduced to below the seeing limit of ${\sim}3''$ . This demonstration allowed an estimate of the uncertainties on the transient sensitivity timescales of the prototype survey of Tingay $\&$ Joubert (2021), with the nominal timescale sensitivity of 21 ms revised to be in the range of $20\!-\!22$ ms, which does not significantly affect the results of the experiment. The correction system will be adopted into the final version of high-cadence imaging experiment, which is currently under construction. The correction system is inexpensive ( $<\!{\$}$ A100) and composed of readily available hardware and is readily adaptable to other applications. Design details and codes are therefore made publicly available.

A Gyro-Aided Strapdown Triaxial Magnetometer Calibration Method Robust to Gyro Bias

Geomagnetic vector field is always measured by a strapdown triaxial magnetometer on a vehicle. The magnetometer measurements are polluted by the magnetometer inherent errors, misalignment error, and vehicle-generated magnetic inferences. With the aid of absolute attitude information from the gyro, the calibration accuracy has been proved to be greatly improved, especially for vector measurements. However, the performance of calibration methods is greatly affected by the gyro bias, which causes error accumulated over time in the absolute attitude information. In this work, we propose an improved calibration method robust to gyro bias based on relative attitude information from the gyro. Simulation results indicated that the proposed method can estimate the calibration parameters more accurately and stably than the traditional method when the gyro bias exists. The influence on the calibration accuracy of the different parts of the gyro bias is also analyzed by simulation. Experiment using a low-cost gyro proved that the proposed method can effectively improve the estimation accuracy of the calibration parameters, as the influence on the traditional method of the gyro bias is 2–3 orders of magnitude higher than that on the proposed method.

Least Squares Finite Element Method for Hepatic Sinusoidal Blood Flow

AbstractThe simulation of complex biological systems such as the description of blood flow in organs requires a lot of computational power as well as a detailed description of the organ physiology. We present a novel Least‐Squares discretization method for the simulation of sinusoidal blood flow in liver lobules using a porous medium approach for the liver tissue. The scaling of the different Least‐Squares terms leads to a robust algorithm and the inherent error estimator provides an efficient refinement strategy.

Determination of diffusion coefficient by image-based fluorescence recovery after photobleaching and single particle tracking system implemented in a single platform

Fluorescence recovery after photobleaching (FRAP) and single particle tracking (SPT) techniques determine the diffusion coefficient from average diffusive motion of high-concentration molecules and from trajectories of low-concentration single molecules, respectively. Lateral diffusion coefficients measured by FRAP and SPT techniques for the same biomolecule on cell membrane have exhibited inconsistent values across laboratories and platforms with larger diffusion coefficient determined by FRAP, but the sources of the inconsistency have not been investigated thoroughly. Here, we designed an image-based FRAP-SPT system and made a direct comparison between FRAP and SPT for diffusion coefficient of submicron particles with known theoretical values derived from Stokes–Einstein equation in aqueous solution. The combined [Formula: see text]FRAP-SPT technique allowed us to measure the diffusion coefficient of the same fluorescent particle by utilizing both techniques in a single platform and to scrutinize inherent errors and artifacts of FRAP. Our results reveal that diffusion coefficient overestimated by FRAP is caused by inaccurate estimation of the bleaching spot size and can be corrected by simple image analysis. Our [Formula: see text]FRAP-SPT technique can be potentially used for not only cellular membrane dynamics but also for quantitative analysis of the spatiotemporal distribution of the solutes in small scale analytical devices.

ПРИМЕНЕНИЕ МЕТОДА НАИБОЛЬШИХ ВКЛАДОВ В ТЕХНИКЕ ИНВЕРСИИ МАГНИТОГРАММ

Fundamentals of the spherical harmonic analysis (SHA) of the geomagnetic field were created by Gauss. They acquired the classical Chapman — Schmidt form in the first half of the XXth century. The SHA method was actively developed for domestic geomagnetology by IZMIRAN, and then, since the start of the space age, by ISTP SB RAS, where SHA became the basis for a comprehensive method of MIT (magnetogram inversion technique). SHA solves the inverse problem of potential theory and calculates sources of geomagnetic field variations (GFV) - internal and external electric currents. The SHA algorithm forms a system of linear equations (SLE), which consists of 3K equations (three components of the geomagnetic field, K is the number of ground magnetic stations). Small changes in the left and (or) right side of such SLE can lead to a significant change in unknown variables. As a result, two consecutive instants of time with almost identical GFV are approximated by significantly different SHA coefficients. This contradicts both logic and real observations of the geomagnetic field. The inherent error of magnetometers, as well as the method for determining GFV, also entails the instability of SLE solution. To solve such SLEs optimally, the method of maximum contribution (MMC) was developed at ISTP SB RAS half a century ago. This paper presents basics of the original method and proposes a number of its modifications that increase the accuracy and (or) speed of solving the SLEs. The advantage of MMC over other popular methods is shown, especially for the Southern Hemisphere of Earth.

APPLYING THE METHOD OF MAXIMUM CONTRIBUTIONS TO THE MAGNETOGRAM INVERSION TECHNIQUE

Fundamentals of the spherical harmonic analysis (SHA) of the geomagnetic field were created by Gauss. They acquired the classical Chapman — Schmidt form in the first half of the XXth century. The SHA method was actively developed for domestic geomagnetology by IZMIRAN, and then, since the start of the space age, by ISTP SB RAS, where SHA became the basis for a comprehensive method of MIT (magnetogram inversion technique). SHA solves the inverse problem of potential theory and calculates sources of geomagnetic field variations (GFV) - internal and external electric currents. The SHA algorithm forms a system of linear equations (SLE), which consists of 3K equations (three components of the geomagnetic field, K is the number of ground magnetic stations). Small changes in the left and (or) right side of such SLE can lead to a significant change in unknown variables. As a result, two consecutive instants of time with almost identical GFV are approximated by significantly different SHA coefficients. This contradicts both logic and real observations of the geomagnetic field. The inherent error of magnetometers, as well as the method for determining GFV, also entails the instability of SLE solution. To solve such SLEs optimally, the method of maximum contribution (MMC) was developed at ISTP SB RAS half a century ago. This paper presents basics of the original method and proposes a number of its modifications that increase the accuracy and (or) speed of solving the SLEs. The advantage of MMC over other popular methods is shown, especially for the Southern Hemisphere of Earth.

Improved GNSS Localization and Byzantine Detection in UAV Swarms.

Many tasks performed by swarms of unmanned aerial vehicles require localization. In many cases, the sensors that take part in the localization process suffer from inherent measurement errors. This problem is amplified when disruptions are added, either endogenously through Byzantine failures of agents within the swarm, or exogenously by some external source, such as a GNSS jammer. In this paper, we first introduce an improved localization method based on distance observation. Then, we devise schemes for detecting Byzantine agents, in scenarios of endogenous disruptions, and for detecting a disrupted area, in case the source of the problem is exogenous. Finally, we apply pool testing techniques to reduce the communication traffic and the computation time of our schemes. The optimal pool size should be chosen carefully, as very small or very large pools may impair the ability to identify the source/s of disruption. A set of simulated experiments demonstrates the effectiveness of our proposed methods, which enable reliable error estimation even amid disruptions. This work is the first, to the best of our knowledge, that embeds identification of endogenous and exogenous disruptions into the localization process.

Error analysis for filtered back projection reconstructions in Besov spaces

Filtered back projection (FBP) methods are the most widely used reconstruction algorithms in computerized tomography (CT). The ill-posedness of this inverse problem allows only an approximate reconstruction for given noisy data. Studying the resulting reconstruction error has been a most active field of research in the 1990s and has recently been revived in terms of optimal filter design and estimating the FBP approximation errors in general Sobolev spaces. However, the choice of Sobolev spaces is suboptimal for characterizing typical CT reconstructions. A widely used model are sums of characteristic functions, which are better modelled in terms of Besov spaces . In particular with α ≈ 1 is a preferred model in image analysis for describing natural images. In case of noisy Radon data the total FBP reconstruction error splits into an approximation error and a data error, where L serves as regularization parameter. In this paper, we study the approximation error of FBP reconstructions for target functions with positive and 1 ⩽ p, q ⩽ ∞. We prove that the L p -norm of the inherent FBP approximation error f − f L can be bounded above by under suitable assumptions on the utilized low-pass filter’s window function W. This then extends by classical methods to estimates for the total reconstruction error.

Seasonal to Decadal Predictions With MIROC6: Description and Basic Evaluation

AbstractThe present paper presents results of seasonal‐to‐decadal climate predictions based on a coupled climate model called the Model for Interdisciplinary Research on Climate version 6 (MIROC6) contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). MIROC6 is initialized every year for 1960–2018 by assimilating observed ocean temperature and salinity anomalies and full fields of sea ice concentration and by prescribing atmospheric initial states from reanalysis data. The impacts of updating the system on prediction skill are then evaluated by comparing hindcast experiments between the MIROC6 prediction system and a previous system based on MIROC version 5 (MIROC5). Skill of seasonal prediction is overall improved in association with representation and initialization of El Niño/Southern Oscillation (ENSO), the Quasi‐Biennial Oscillation (QBO), and the Northern Hemisphere sea ice concentration in MIROC6. In particular, the QBO is skillfully predicted up to 3 years ahead with a maximum anomaly correlation exceeding r = 0.8. The prediction skill for the North Atlantic Oscillation in winter is also enhanced, but the prediction still suffers from model's inherent errors. On decadal timescales, MIROC6 has a larger fraction of areas of the globe with better surface temperature skill at all lead times than MIROC5, and it has predictive skill in the annual‐mean sea surface temperature (SST) in the North Atlantic and the Pacific. In particular, MIROC6 hindcasts at 2–5 years lead time are able to capture the spatial structure of SST changes in the North Pacific and the eastern tropical Pacific associated with the 1970s regime shift better than MIROC5 hindcasts.

INS-DVL Navigation Improvement Using Rotational Motion Dynamic Model of AUV

INS-DVL integration is a common method for underwater navigation. However, inherent errors of sensors, especially in MEMS IMUs, lead to inaccuracies in estimating the position and attitude. In this paper, dynamic motion model of AUV is used to improve MEMS INS-DVL navigation. In this method, which is called model-aided or model-based navigation, the information of the kinetic model of the vehicle (obtained from Newton-Euler equations) is used to improve the navigation performance. Previous model-aided navigation studies about AUVs have been focused on the translational dynamic model of vehicles. As the best of our knowledge, this paper is the first one which suggests using a rotational motion model of AUVs to improve the navigation performance and at the same time, reports successful implementation of method through field-testing. The utilized dynamic model, describes the rotational motion of AUV in a 3DOF form. Information extracted from the rotational dynamic model and the outputs of gyroscopes, will be fused in a secondary Kalman filter. So, before the fusion with auxiliary sensors in the main Kalman filter, the gyroscopes outputs have been improved by the rotational motion model. Therefore, the overall output will be better than the outputs of gyroscopes by itself. The main Kalman filter is a perturbation-based Kalman filter which is employed for data fusion. This algorithm is implemented on real data collected from field-test. The results show that this inexpensive method which requires no additional equipment was truly effective in improving navigation performance. Using this model, the accuracy of navigation was improved 5 to 10 times.

Forward-Aware Information Bottleneck-Based Vector Quantization for Noisy Channels

The main focus will be on the indirect Joint Source-Channel Coding problem in which a noisy observation of the source has to be quantized ahead of transmission over an error-prone forward link to a remote processing unit. To that end, we present here a complete extension to the preliminary Information Bottleneck method by providing the formal optimal solution to this newly established Variational Principle , together with an algorithm, the Forward-Aware Vector Information Bottleneck (FAVIB) , to pragmatically tackle its underlying non-convex design optimization. FAVIB extends the current state-of-the-art approaches via capacitating a full sweep over the entire gamut of the trade-off parameter. Consequently, the trajectory of all achievable points in the Information-Compression plane becomes traversable via soft mappings. It will be shown that, by enjoying an inherent error protection, this novel compression scheme can obviate the call for separate channel coding on the forward path.

LEVAX: An Input-Aware Learning-Based Error Model of Voltage-Scaled Functional Units

As Moore’s Law comes to an end and transistor scaling increasingly falls short in improving energy efficiency, alternative computing paradigms are direly needed. This need is further highlighted by the overwhelming increase in computing demand posed by emerging applications, such as multimedia and data analysis. Fortunately, such driving workloads also present new opportunities since, thanks to their inherent error tolerance, they do not require completely accurate computations. Thus, by trading off accuracy for better performance or improved efficiency, approximate computing promises tremendous growth for future computing. Various approximation methods demonstrate the effectiveness of voltage scaling in functional units (FUs) for exploring this energy-error tradeoff. Yet, while an accurate error model is critical for assessing the error behavior of voltage-scaled FUs and its effects on application quality, existing error models of voltage-scaled FUs overlook the effects of input data and error rate disparity among different bits. To tackle this challenge, we propose LEVAX, an input-aware learning-based error model of voltage-scaled FUs that can predict the timing error rate (TER) for each output bit. This model is trained using random forest methods, with input features and output labels extracted from gate-level simulations. To validate its effectiveness and demonstrate its prediction accuracy, we use LEVAX on various FUs. Across all bit positions, voltage levels, and FUs, LEVAX achieves, on average, a relative error of 1.20%. LEVAX also achieves an average per-voltage root mean square error (RMSE) of 1.03% and per-bit RMSE of 1.17%. Exposing this error rate even up to the application level, LEVAX can estimate the quality of four image processing applications under-voltage scaling with an average accuracy of 97.9%. To the best of our knowledge, LEVAX is the first voltage scaling error model of FUs that can incorporate the effects of input data.

Deep In-Memory Architectures in SRAM: An Analog Approach to Approximate Computing

This article provides an overview of recently proposed deep in-memory architectures (DIMAs) in SRAM for energyand latency-efficient hardware realization of machine learning (ML) algorithms. DIMA tackles the data movement problem in von Neumann architectures head-on by deeply embedding mixed-signal computations into a conventional memory array. In doing so, it trades off its computational signal-to-noise ratio (compute SNR) with energy and latency, and therefore, it represents an analog form of approximate computing. DIMA exploits the inherent error immunity of ML algorithms and SNR budgeting methods to operate its analog circuitry in a low-swing/low-compute SNR regime, thereby achieving >100× reduction in the energy-delay product (EDP) over an equivalent von Neumann architecture with no loss in inference accuracy. This article describes DIMA's computational pipeline and provides a Shannon-inspired rationale for its robustness to process, temperature, and voltage variations and design guidelines to manage its analog nonidealities. DIMA's versatility, effectiveness, and practicality demonstrated via multiple silicon IC prototypes in a 65-nm CMOS process are described. A DIMA-based instruction set architecture (ISA) to realize an end-to-end application-toarchitecture mapping for the accelerating diverse ML algorithms is also presented. Finally, DIMA's fundamental tradeoff between energy and accuracy in the low-compute SNR regime is analyzed to determine energy-optimum design parameters.

Gas-phase thermography of droplet combustion and its application to characterize nanofuels

An IR thermography-based methodology is proposed to compute the instantaneous burning rate in droplet combustion. Temperature distribution around the droplet surface is measured using an infrared camera with a narrow bandpass filter of 4.25-μm wavelength. The technique holds importance in multicomponent fuels, densely concentrated nanofuels and nanofuels prepared by the addition of surfactant wherein the droplet surface regression rate is not linear due to micro explosions. For such applications, the conventional high-speed videography shows limitations, highlighting the importance of IR thermography. The methodology has first been validated against the results of classical interferometry. Thereafter, it has been applied to characterize the droplet combustion of alumina nanoparticles (AlONPs)-loaded methanol nanofluids. The results of the study showed that burning rates calculated through IR thermography are in good agreement with those determined using the conventional videography approach for the low mass loading of alumina nanoparticles. However, at higher mass loading ratios, the random occurrence of microexplosions towards the end of droplet combustion leads to inherent errors in the computation of the mass burning rate through videography. An increase in the average burning rate of the combusting droplet is observed at higher values of mass loading ratios, an observation that has been explained using IR thermography measurements.

Sensorless parameter estimation of vanadium redox flow batteries in charging mode considering capacity fading

State of Charge (SoC) and discharge capacity of the batteries are parameters that cannot be determined directly from the battery monitoring and control system and requires estimation. Current and voltage sensors have inherent error and delay leading to inaccurate measurements leading to inaccurate SoC and discharge capacity estimations. These sensors also have an additional cost to the battery system. This paper proposes a sensorless approach to estimate parameters of Vanadium Redox Flow Batteries (VRFBs) for both CC and CV charging methods by estimating battery current in CV mode and terminal voltage in CC mode. The results of estimations by the sensorless approach show a maximum relative error of 0.0035 in estimating terminal voltage in CC charging and a maximum relative error of 0.045 in estimating charging current in CV mode. Furthermore, long-term operation of vanadium redox flow batteries causes ion diffusions across the membrane and the depletion of active materials, which leads to capacity fading in VRFBs and inaccurate SoC estimation. To address the inaccuracy of SoC estimation in the long-term use of the battery, the capacity fading model is also considered for VRFBs in this paper. Experimental results show a 19% electrolyte volume change in the positive and negative tanks after 200 cycles of charge/discharge due to the bulk electrolyte transfer between the positive and negative sides of the battery system. This change of electrolyte volume results in 13.73% capacity fading after 200 cycles of charging/discharging. The SoC also changes by 7.1% after 200 cycles, due to the capacity and electrolyte volume loss, which shows the necessity of considering capacity fading in long-term use of the battery.

A Review of Fine Blanking: Influence of Die Design and Process Parameters on Edge Quality

Conventional blanking is the single-stroke shearing of closed contour profiles. However, the blanked pieces have inherent errors like fractured cut surface and blank dishing necessitating further sizing and/or finishing operations. Fine blanking is a more precise version of the blanking operation with lesser errors such that post-forming steps required will be minimal. Thus, it is an ideal candidate for manufacturing precision machine components. This review paper starts with the introduction to the process. Then, studies aimed at the analysis of fine blanking tooling and process parameters and their effect on fine-blanked part characteristics are presented. Inferences from studies on the design of equipment for fine blanking, deformation mechanics involved and the fine blanking performance of different materials are also discussed. The use of finite element simulation studies to understand the process better has also been determined. Various reported strain measurement techniques are then discussed. The recent improvement in techniques like in situ digital image correlation enables accurate experimental measurement of strain in the shear zone which can be used to validate the findings from FE simulations. Fine blanking process modifications that aimed at cost reduction or improvement in product quality are summarized as well.

Toward Translating Raw Indoor Positioning Data into Mobility Semantics

Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into mobility semantics that describe a moving object’s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the cleaning layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the annotation layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a semantic matching method that makes proper annotations for split snippets. In the complementing layer, we devise an inference method that makes use of the indoor topology and the mobility semantics already obtained to recover the missing mobility semantics. The extensive experiments demonstrate that our solution is efficient and effective on both real and synthetic data. For typical queries, our solution’s resultant mobility semantics lead to more precise answers but incur less execution time than alternatives.

Microkinetic Modeling: A Tool for Rational Catalyst Design.

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.