Automatic Calibration Technique Research Articles

ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs

Infrared thermography is an emerging technique in biomedical research, potentially providing diagnostic insights into psychological stress, physical strain, muscle fatigue, inflammation, tissue damage, and diseases with thermogenic effects. However, manual analysis strategies are frequently applied causing incomparable, non-reproducible results and hampering standardization. Moreover, widely applied manual analysis cannot recognize blood vessel-related thermal radiation patterns during physical exercise. Therefore, an enhanced processing pipeline, “ThermoNet”, has been developed to automatically process thermograms captured during running. For acquisition, an automatic temperature calibration technique has been introduced to obtain reliable pixel-temperature mapping. The thermograms are semantically segmented in the processing pipeline to extract the anatomical regions of interest (ROIs) by a state-of-the-art deep neural network rather than considering both legs as a single area. A second neural network further examines the ROIs to identify different venous and arterial (perforator) patterns. Within the segments, advanced statistical features are computed to provide time series data. Separate analysis of venous and perforator vessel patterns is carried out on individual connected components, resulting in the extraction of 276 features for each thermogram. The enhanced ROI extraction achieved a high accuracy for the left and right calf on the manually annotated test set. Each step of the ThermoNet pipeline represents a significant improvement over previous analysis methods. Finally, ThermoNet is a transferable pipeline for automatic, reproducible, and objective analysis of ROIs in thermal image sequences of moving test individuals.

Identifying manning roughness coefficient using automatic calibration method and simulation of pollution incidents in the Nile River, Egypt

Study regionA reach of the Nile River located between Naga Hammadi barrage and Asyut barrage, Egypt Study focusAn accurate representation of hydrodynamics of an important water source helps cope with expected future climate changes, pollution incidents and water quality problems. Here, a comparison between HEC-RAS 1D and TELEMAC-2D model was conducted by identifying different Manning coefficients. Moreover, an automatic calibration using Dual-Annealing optimization method was applied for first time to calibrate the model with non-uniform Manning coefficients. The transport of tracer (pollution) was simulated by computing tracer residence times. Pollution transport scenarios were discussed to draw a picture of pollution incidents which will continue to happen in the future. An equation indicating the relation between flow discharge and residence time was derived to hurriedly help decision makers in water management during sudden pollution incidents. New hydrological insights for the regionA model with spatially variable Manning coefficients using TELEMAC-2D was set up and calibrated achieving good accuracyies with average errors of approximately 4 cm and 7 cm between field and simulated water levels for two different discharge scenarios. Moreover, an equation for relation between flow discharge and residence time was derived producing a strong correlation coefficient of 0.95. This study, integrating advanced hydrodynamic models and automatic calibration techniques, provides a robust framework for assessing and managing water resource challenges under varying flow conditions.

Assessment of contamination dispersion in a porous aquifer environment using explicit and implicit methods in the MODFLOW–MODPATH model

This study presents a meticulously edited article that assesses the uncertainty associated with contamination dispersion in a porous aquifer environment. The article employs a conceptual model incorporating finite difference flow modeling and particle tracking methods grounded in particle movement theory within groundwater flow. The groundwater flow model utilizes an automatic calibration technique, PES, and independent verification, to minimize statistical discrepancies in the optimized parameters. The statistical summary of the model output reveals the average pollution concentration in the study area, specifically the landfill aquifer in northern Iran. The results indicate that the initial contamination value at the boundaries is zero, whereas 91,802 units are discharged into the sea. Moreover, approximately 3,317,290,003 pollution units have been extracted from pumping wells along the contamination plume over 10 years. Furthermore, the drainage network releases 1,778,680,001 pollution units, resulting in a net outflow of 5,082,740,007 units from the same drainage network. Additionally, 9,912,180,003 units were discharged from the leaky boundaries. It should be noted that these statistics can be reversed depending on the specified period. The difference between the input and output pollution values in the study area indicates a net accumulation of 8192 units. To analyze the sensitivity of contamination dispersion in the porous aquifer environment and the variations in coastal drainage patterns obtained from the finite difference simulation, the explicit and implicit methods, as well as the random walk particle tracking method, were investigated in a portion of the basin area using the South Side Implicit method. One of the main objectives of this sensitivity analysis is to evaluate the results of the uncalibrated qualitative model and examine the particle dispersion in the adjacent area of the contaminated landfill region to assess the error of the proposed model by developing a limited test. The calculated error or computational difference between the explicit and implicit methods and the applet model is considered negligible. For example, the calculated error between the explicit and backward methods and the applet model ranges from 0.086 to 0.372 units for a concentration interval of 0.512 units and 0.680 units for a time interval 0.537. Therefore, the computational differences are not significant. Furthermore, based on theoretical sensitivity analysis of contamination dispersion, the laboratory model demonstrates that the flow direction in this modeling is from west to east, as expected.

Automatic model calibration of combined hydrologic, hydraulic and stormwater quality models using approximate Bayesian computation

A range of automatic model calibration techniques are used in water engineering practice. However, use of these techniques can be problematic due to the requirement of evaluating the likelihood function. This paper presents an innovative approach for overcoming this issue using a calibration framework developed based on Approximate Bayesian Computation (ABC) technique. Use of ABC in automatic model calibration was undertaken for a combined urban hydrologic, hydraulic and stormwater quality model. The simulated runoff hydrograph and total suspended solid (TSS) pollutograph were compared with observed data for multiple events from three different catchments, and found to be within 95% confidence intervals of the simulated results. The R programmed model was validated by comparing simulated flow with similar commercially available modeling software, MIKE URBAN output determined using mean value of parameters obtained from the calibration exercise, and performed well by satisfying statistical criteria's such as coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME). The developed framework is useful for automatic calibration and uncertainty estimation using ABC approach in complex hydrologic, hydraulic and stormwater quality models with multi-input-output systems.

Bayesian calibration of GPU–based DEM meso-mechanics Part I: Parallelization of RVEs

Calibration of Discrete Element Method (DEM) parameters is essential for modeling geotechnical applications. This task can, however, be extremely tedious or sometimes even impossible to undertake. This is largely due to two issues namely: (1) a large sample size of DEM simulations and number of sampling iterations are necessary to accurately infer the probability distribution of a model over a large parameter space and (2) DEM is computationally intractable compared to other numerical methods. In the scope of reducing the number of sampling iterations, automatic calibration techniques are available to extract and make use of the hidden contact mesostructure correlations through adaptive sampling. Coincidentally, to improve computational speed, significant advances toward Graphics Processor Unit (GPU) based DEM algorithms have been achieved over the past years on particle parallelism. Nevertheless, the problem remains that DEM simulations are serialized during the calibration processes. While the companion paper addresses parameter calibration, this study presents a novel algorithm to parallelize independent simulations within a sample set. The selected system is the Representative Volume Element (RVE) which is widely used in geotechnics for solving soil response in the static regime. The algorithm includes the following key features: (1) simulation level parallelism of non-interacting RVEs through highly efficient hierarchical memory groups and access patterns (2) a low latency and memory-efficient implementation of deformable periodic boundary conditions (PBC) which uses lookup tables and bitmasks (3) modified Uniform Grid and Bounding Volume Hierarchy (BVH) contact detection algorithms which partitions the RVE index into the hashing keys. The drained DEM triaxial compression is used to validate the algorithm on dry graded quartz. Three performance degrading factors for the calibration processes are considered: (1) the number of particles per RVE (2) calibration sample size and (3) sequential launch time per calibration step. This algorithm shows a factor of about 9.8 times speedup when parallelizing 100 DEM RVEs in one batch.

Automatic calibration of multiple cameras and lidars for autonomous vehicles

Autonomous navigation of unmanned vehicles (UVs) is currently one of the most interesting scientific and technical problems, and this is even more true for UVs moving across rough terrain. As a rule, such vehicles are equipped with different sensors operating simultaneously, and sophisticated software is being developed to collect and analyze miscellaneous data. For the joint use of data from multiple cameras and lidars, all of them need to be mutually referenced in a single coordinate system. This problem is solved in a process of system calibration. The general idea is to place an object of special type in the field of view of the sensors, so that its characteristic points can be automatically detected from different points of view by all sensors. Then the repeated survey of the object allows you to create the required number of tie points for mutual alignment of the sensors. This work presents a technique for automatic calibration of a system of cameras and lidars using an original calibration object. The presented results of the experiments show that the calibration accuracy is sufficiently high.

Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods

In this study, a grey box (GB) model for simulating internal air temperatures in a naturally ventilated nearly zero energy building (nZEB) was developed and calibrated, using multiple data configurations for model parameter selection and an automatic calibration algorithm. The GB model was compared to a white box (WB) model for the same application using identical calibration and validation datasets. Calibrating the GB model using only one week of data produced very accurate results for the calibration periods but led to inconsistent and typically inaccurate results for the validation periods (root mean squared error (RMSE) in validation periods was 229% larger than the RMSE in calibration periods). Using three weeks of data from varying seasons for calibration reduced the model accuracy in the calibration period but substantially increased the model accuracy and generalisation abilities for the validation period, reducing the mean RMSE by over 160%. The use of one week of data increased the standard deviation in parameter selections by over 40% when compared with the three-week calibration datasets. Utilising data from multiple seasons for calibration purposes was found to substantially improve generalisation abilities. When compared to the WB model, the GB model produced slightly less accurate results (mean RMSE of the GB model was 1.5% higher). However, the authors found that employing GB modelling with an automatic model calibration technique reduced the human labour input for simulating internal air temperature of a naturally ventilated nZEB by approximately 90%, relative to WB modelling using a manually calibrated approach.

A Tool for Annotating Homographies from Hockey Broadcast Video

In order to develop solutions for automatic ice rink localization from broadcast video, a dataset with ground truth homographies is required. Hockey broadcast video does not tend to provide camera parameters for each frame, which means that they must be gathered manually. A novel tool for collecting ground truth transforms through point correspondences between each frame and an overhead view of the ice rink is presented in this paper. Through collaboration with the users of the tool, we have added features to improve accuracy and efficiency, especially in frames with few lines on the playing surface visible. A dataset of 4,262 frames has been collected, which will be used for research into automatic camera calibration techniques.

Parameters Estimation at Ungauged Catchments Using Rainfall-Runoff Model, Upper Tekeze Basin, Ethiopia

This study was conducted for parametres estimation and stream flow prediction at ungauged catchments on the case of Upper Tekeze basin, Ethiopia by using Rainfall-runoff model. In the basin, most of the catchments were ungauged. The basin has 9199km2 and 3638km2 gauged and ungauged catchments respectively. Rainfall and stream flow data were analyzed in the period of 1992-2006 and 1992-20006, respectively. Parameters calibrated for gauged catchments were extrapolated to ungauged catchments on the base of similar physical catchment characteristics using regionalization techniques. Regionalization methods such as multiple linear regression, spatial proximity, sub basin mean and area ratio were applied to transfer model parameters values from gauged to ungauged catchments. For this study seven gauged catchments were satisfied objective functions in the calibrated and validation period, for example in Gheba catchment Nash-Sutcliffe model efficiency coefficient (NSE), relative volume error (RVE) and coefficient of determination (R2) were, 0.81, -4.25, 0.77 and 0.71, 5.5, 0.74 respectively. Stream flow simulation at ungauged catchments by using spatial proximity and sub basin mean method were contributing high runoff volume compare to other methods. The result for this study shows that the Key model parameters like runoff coefficient (Beta), recession coefficient of upper reservoir zone (Khq), Limit for evapotranspiration (Lp), field capacity (Fc), percolation (Perc) as defaulting value when applying HBV-96 model to the future regionalization studies. Model parameters were calibrated manually by try and error rules, however it was tidies therefore more creative automatic model calibration techniques could be useful for upcoming studies. Thus, Current and future water resources development endeavors may use apply such discharge data for planning and design purposes.

Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring

Sponge city construction (SCC) in China, as a new concept and a practical application of low-impact development (LID), is gaining wide popularity. Modelling tools are widely used to evaluate the ecological benefits of SCC in stormwater pollution mitigation. However, the understanding of the robustness of water quality modelling with different LID design options is still limited due to the paucity of water quality data as well as the high cost of water quality data collection and model calibration. This study develops a new concept of ‘robustness’ measured by model calibration performances. It combines an automatic calibration technique with intensive field monitoring data to perform the robustness analysis of storm water quality modelling using the SWMM (Storm Water Management Model). One of the national pilot areas of SCC, Fenghuang Cheng, in Shenzhen, China, is selected as the study area. Five water quality variables (COD, NH3-N, TN, TP, and SS) and 13 types of LID/non-LID infrastructures are simulated using 37 rainfall events. The results show that the model performance is satisfactory for different water quality variables and LID types. Water quality modelling of greenbelts and rain gardens has the best performance, while the models of barrels and green roofs are not as robust as those of the other LID types. In urban runoff, three water quality parameters, namely, SS, TN and COD, are better captured by the SWMM models than NH3-N and TP. The modelling performance tends to be better under heavy rain and significant pollutant concentrations, denoting a potentially more stable and reliable design of infrastructures. This study helps to improve the current understanding of the feasibility and robustness of using the SWMM model in sponge city design.

Assessment of Groundwater Recharge in Agro-Urban Watersheds Using Integrated SWAT-MODFLOW Model

Numerical models are employed widely to evaluate the hydrological components of a watershed but, traditionally, watershed models simplify either surface or subsurface flow module. In this setup, as a bridge between groundwater and surface water regimes, aquifer recharge is the most affected segment of the water balance. Since the watershed processes are increasingly changed, the need for a comprehensive model with detailed conceptualizing capacity of both groundwater and surface water flow systems is growing. This work focuses on the spatiotemporal groundwater recharge assessment in gauged and ungauged agro-urban watersheds in South Korea using the updated SWAT-MODFLOW model, which integrates the Soil and Water Assessment Tool (SWAT2012) and Newton–Raphson formulation for Modular Finite Difference Groundwater Flow (MODFLOW-NWT) in a single executable code. Before coupling, the setup, calibration, and verification of each model were performed separately. After integration, irrigation pumps and drain cells mapping to SWAT auto-irrigation and subbasins were initiated. Automatic calibration techniques were used for SWAT and MODFLOW-NWT models, but a manual calibration was used for the integrated model. A physical similarity approach was applied to transfer parameters to the ungauged watershed. Statistical model performance indicators revealed that the low streamflow estimation was improved in SWAT-MODFLOW. The spatiotemporal aquifer recharge distribution from both the stream seepage and precipitation showed a substantial change, and most of the aquifer recharge occurs in July–September. The areal annual average recharge reaches about 18% of the precipitation. Low-lying areas receive higher recharge consistently throughout a year. Overall, SWAT-MODFLOW exhibited reasonable versatility in evaluating watershed processes and produced valuable results with reasonable accuracy. The results can be an important input for policymakers in the development of sustainable groundwater protection and abstraction strategies for the region.

Auto‐calibration of position offset for PMSM drives with uncertain parameters

This Letter proposes a novel technique for automatic position offset calibration for permanent-magnet synchronous machine (PMSM) drives. Contrary to existing methods, the proposed technique estimates and compensates for the position offset without using machine true parameters or signal injection, which is possible under the condition of zero-current control at high enough speed where parameter-dependent terms are eliminated. To achieve this condition using uncertain parameters, speed control with absolute value feedback and model-free current control are devised. Additionally, under this condition, not only the misalignment of the position sensor but also the sensor delay can be considered as the position offset, which improves the accuracy of the calibration. The proposed approach will be verified by experimental results.

A 12.3-μW 0.72-mm² Fully Integrated Front-End IC for Arterial Pulse Waveform and ExG Recording

In this article, a power and area efficient front-end (FE) IC for biomedical instrumentation is presented. In addition to ExG recording, the proposed system provides a noninvasive arterial pulse waveform (APW) detection through direct skin contact with a packaged silicone-coated resistive-bridge pressure sensor. An automatic calibration technique is proposed to compensate for the resistive-bridge offset and reduce it down to 1 mV. High linearity in the FE is provided through the use of an instrumentation amplifier (IA) with a super class-AB output stage. The IA is followed by a single block for programmable gain amplification (PGA), low pass filtering (LPF), and analog-to-digital converter (ADC) functionality. The PGA-LPF-ADC has a reconfigurable gain from 6 to 14 dB and can cover a bandwidth from 100 to 1 kHz, saving 90% of the passives area and 25% of the FE power. Together with on-chip low dropout regulators (LDOs) and reference buffers, the prototype fabricated in 0.18- $\mu \text{m}$ CMOS technology has an active area of 0.72 mm2 and consumes a 12.3- $\mu \text{W}$ dc power. The APW (ExG) FE measures an input-referred noise of 5.84 (1.22) $\mu \text{V}_{\text {rms}}$ with a total-harmonic distortion of −80 (−81.8) dB.

Auto-Resonant Tuning for Capacitive Power and Data Telemetry Using Flexible Patches

This brief proposes and demonstrates a reliable power and data transfer scheme for biomedical implants through a resonant capacitive link. Flexible, conformable, and biocompatible patches are adopted with an automatic resonant frequency calibration technique for the first time. A voltage feedback network, a Class- ${D}$ driver, and a custom algorithm have been developed for the low overhead calibration. An ex vivo chicken skin tissue-based experiment with 4 cm2 flexible, conformal capacitive patches demonstrated the method. Experimental results show that the power delivered to the load (PDL) of the link extends up to 150 mW and maximum power transfer efficiency (PTE) of the link is 54%. A simultaneous data transfer with a rate of up to 170 kbps alongside power transfer using 1.5 to 2 mm-thick biological skin tissue has been achieved.

Template Calibration Parameter Optimization of Fuzzy Vault Method

In the concrete implementation of the fuzzy vault algorithm, the geometric hash method is a common technique for automatic calibration of biometric templates. For the fuzzy problem of parameter acquisition, the matching accuracy of fuzzy vault template is affected in the three parameters: the pixel size, hash table and hash table quantization parameters ([Formula: see text] and [Formula: see text]). The single factor experiment method obtains the optimal range of these three parameters, and the extraction range of the fuzzy point and the selection rule of the base point distance are improved for the fuzzy vault algorithm. Finally, based on the FVC fingerprint database, their matching precision is compared for the algorithm before and after optimization. The experimental results show that the false rejection rate (FRR) of the optimized algorithm is reduced by at least 9.84%, and the false acceptance rate (FAR) is reduced by at least 7.12%, indicating that the optimization scheme improves the matching accuracy of the algorithm. The algorithm has certain robustness and practicability.

Optimization of the Multi-Start Strategy of a Direct-Search Algorithm for the Calibration of Rainfall–Runoff Models for Water-Resource Assessment

Calibration of conceptual rainfall–runoff models (CRRM) for water-resource assessment (WRA) is a complicated task that contributes to the reliability of results obtained from catchments. In recent decades, the application of automatic calibration techniques has been frequently used because of the increasing complexity of models and the considerable time savings gained at this phase. In this work, the traditional Rosenbrock (RNB) algorithm is combined with a random sampling method and the Latin hypercube (LH) to optimize a multi-start strategy and test the efficiency in the calibration of CRRMs. Three models (the French rural-engineering-with-four-daily-parameters (GR4J) model, the Swedish Hydrological Office Water-balance Department (HBV) model and the Sacramento Soil Moisture Accounting (SAC-SMA) model) are selected for WRA at nine headwaters in Spain in zones prone to long and severe droughts. To assess the results, the University of Arizona’s shuffled complex evolution (SCE-UA) algorithm was selected as a benchmark, because, until now, it has been one of the most robust techniques used to solve calibration problems with rainfall–runoff models. This comparison shows that the traditional algorithm can find optimal solutions at least as good as the SCE-UA algorithm. In fact, with the calibration of the SAC-SMA model, the results are significantly different: The RNB algorithm found better solutions than the SCE-UA for all basins. Finally, the combination created between the LH and RNB methods is detailed thoroughly, and a sensitivity analysis of its parameters is used to define the set of optimal values for its efficient performance.

An Automatic On-Chip Calibration Technique for Static and Dynamic DAC Error Correction in High-Speed Continuous-Time Delta-Sigma Modulators

Calibration is preferred over dynamic element matching (DEM) techniques to correct nonlinearity in digital-to-analog converters (DACs) in high-speed multi-bit continuous-time (CT) delta-sigma modulators (DSMs) to avoid introducing extra excess loop delay. The state-of-the-art calibration techniques, however, involve substantial external control, making it difficult for a complete on-chip realization. In this paper a highly automated on-chip technique for calibrating differential nonlinearity (DNL) and inter-symbolinterference (ISI) errors of a multi-bit DAC in a CTDSM is presented. The proposed technique implements a Calibration Control System (CCS), equipped with a Finite State Machine (FSM) logic, that automates the entire calibration process with minimal intervention. The calibration loop utilizes the modulator itself to produce the digital estimates of the DNL and the average ISI errors of each unit element of the DAC. These digital estimates are then used to configure auxiliary DACs for correction of the errors in the main DAC. For every unit element in the main DAC, an 8-bit dynamic auxiliary DAC injects a calibrated compensation current in the loop at every up-transition of the input data to cancel the average ISI error, and a 5-bit constant current source array corrects its DNL error. Design considerations and post-layout simulation results for a 50-MHz bandwidth, 1.8GS/s 4 th -order CTDSM are presented. The modulator has a 4.8 dB and a 10.8 dB improvement in SNDR and SFDR respectively with calibration, leading to a dynamic range of 71 dB with a total power consumption of 37.7 mW from 1.3 V and 1 V supplies.

COMPARISON OF AUTOMATIC CALIBRATION TECHNIQUES FOR SIMULATING STREAMFLOW IN TROPICAL CATCHMENT

Efficacy of hydrological model strongly depends on model calibration. A number of methods has been developed and employed for optimization of hydrological model so that model can replicate the observed streamflow accurately. A study has been carried out in this paper to find the best parameter optimization method for the calibration of a daily rainfall-runoff model for streamflow simulation of a tropical catchment. For the purpose, seven well known parameter optimization methods namely, Uniform Random Search, Pattern Search, Multi Start Pattern Search, Rosenbrock Method, Multi Start Rosenbrock Search, Genetic Algorithm and Suffled Complex Evaluation are used for the calibration of a conceptual hydrological model known as SIMHYD. Performance of the methods is evaluated by using Nash-Sutcliff coefficient and correlation coefficient. The result indicates that evolutionary algorithm based optimization techniques can optimize the model parameters more accurately for precise calibration of model and reliable prediction of daily streamflow in a tropical catchment.

A methodology for auto-calibrating urban building energy models using surrogate modeling techniques

Owners of large building portfolios such as university campuses have long relied on building energy models to predict potential energy savings from various efficiency upgrades. Traditional calibration procedures for individual building model are time intensive and require specially trained personnel, making their applications to campuses with hundreds of buildings prohibitive. Recently proposed automatic calibration techniques reduce the manual effort during calibration but require hundreds of thousands of energy simulations which increase their cost. To reduce the computational effort of these methods, this paper proposes a methodology that uses a data-driven approximation technique. Instead of brute-force simulations using detailed engineering models, this study employs statistical surrogate models with an optimization algorithm to estimate properties of unknown building parameters. Results demonstrate that when envelope information is available, this workflow yields sufficiently accurate estimates of hard to observe building characteristics, about 500 times faster than traditional approaches.

An Interference-Robust Reconfigurable Receiver With Automatic Frequency-Calibrated LNA in 65-nm CMOS

An interference-robust reconfigurable receiver in 65-nm CMOS is presented. The front end is split into a low-band (LB) RF path (0.1–1.5 GHz) and a high-band (HB) RF path (1–5 GHz). By utilizing a harmonic recombination technique, the LB path could reject the third /fifth-order harmonic interferences. A tunable narrowband dual-feedback common-gate low-noise amplifier (LNA) with $LC$ resonant load provides second-order bandpass filtering to reject the harmonic interferences in the HB path. The RF high-Q bandpass filtering based on the voltage-mode passive mixer and the current-mode low-pass filter in the analog baseband improves the receiver’s resilience to out-of-band interferences. A novel power-detection-based automatic frequency calibration technique is proposed to calibrate the operating frequency of the LNA in the HB path and overcome the effects of process, voltage, and temperature variations. The presented receiver has been implemented in a 65-nm CMOS and consumes 20–76-mW power from 1.2-V power supplies, with a core die area of 5 mm2. The measured results show that the receiver can tolerate −5-dBm interference with 16-dB noise figure (NF) and achieve 95–105-dB maximum conversion gain and 1.7–8-dB NF over 0.1–5 GHz. It also achieves an average harmonic rejection (HR3)/HR5 of 61/68-dB, +7.1/+14.4 dBm in-band/out-of-band input third-order intercept point (OB-IIP3), +71.2-dBm OB-IIP2, and 58.1-dB-image rejection, after the digitally assisted calibrations. The system-level measurements show that the presented receiver achieves 2.1% error vector magnitude (EVM) for 850-MHz Global System for Mobile Communication signals and 5% EVM for band 42 time division duplexing-local thermal equilibrium (LTE) signals, respectively.