Approximate 3D Research Articles

Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters

We generated synthetic equiaxed grain structures using computer graphics software to explore the relationship between various grain size determination methods and true three-dimensional (3D) grain diameters. Mirroring grain measurement techniques, the synthetic 3D grain structures are imaged as 2D micrographs which are measured to yield 1D grain size parameters. Synthetic grain structures provide data at a mass scale and permit exploration of both polished and fractured surface micrographs, revealing one-to-one correspondence between exposed 2D grain cross-sections and individual 3D grains. Analysis of this correspondence yielded a procedure to approximate 3D equiaxed grain size and volume distributions based on the mode of the 2D fractograph grain size distribution. The 3D approximation procedure is shown to be less susceptible to different imaging conditions that affect small, undiscernible grains compared to the standard planimetric and linear intercept methods, which by design also tend to underestimate the 3D grain diameter. The procedure requires larger sample sizes to lower variance and a deeper analysis which could become more practical with machine learning (ML) models for grain boundary segmentation, which synthetic grain structures can help train. This work lays the foundation for analyzing other grain distributions such as columnar and composite grains in similar depth.

Low-velocity streams inside the planetary nebula H2-18

Aims. Numerous planetary nebulae show complicated inner structures that are not obvious to explain. For one such object, we undertook a detailed 3D photoionisation and kinematical model analysis to gain a better understanding of the underlying shaping processes. Methods. We obtained 2D ARGUS/IFU spectroscopy covering the whole nebula in selected representative emission lines. We used 3D photoionisation modelling to compute images and line profiles, and a comparison of the observations with the models was performed to fine-tune the model details. This procedure predicts the approximate nebular 3D structure and kinematics. Results. We find that within a cylindrical outer nebula, there is a hidden, very dense bar-like or cylindrical inner structure. Both structures are co-axial and are inclined to the sky by 40 deg. We propose that the wide, asymmetric, one-sided plume is a flat structure attached to one end of the bar. All nebular components share the same kinematics, with an isotropic velocity field that monotonically increases with distance from the star before reaching a plateau. The relatively low velocities indicate that the observed shapes do not require particularly energetic processes, and there is no indication for the current presence of a jet. The 3D model reproduces the observed line ratios and the detailed structure of the object significantly better than previous models.

A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources.

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms.

A robust five-unknowns higher-order deformation theory optimized via machine learning for functionally graded plates

This article presents a new kind of higher-order deformation theory, called Parametric Higher-order Deformation Theory (PHDT), for the static analysis of functionally graded plates (FGPs). The novelty of the PHDT is the use of strain shape functions that are calibrated by a set of tuning parameters to approximate 3D results along the plate thickness. The tuning parameters are assumed to vary with side-to-thickness ratios and power-law indexes. In contrast to higher-order shear deformation theories (HSDTs), the PHDT is not mathematically constrained to satisfy the traction-free boundary condition on the bottom plate’s surface. The proposed plate model is based on a 5-unknown HSDT previously presented by one of the authors. The governing equations are derived from the principle of virtual works, and Navier-type closed form solutions have been obtained for simply supported FGPs subjected to bisinuisoidal transverse pressure. A general methodology that uses genetic algorithms to determine the optimal tuning parameters of PHDTs for FGPs with various side-to-thickness ratios and power-law indexes is presented. The accuracy of the PHDT is assessed by comparing the results of numerical examples with a 3D elasticity solution, HSDTs reported in the literature, and the well-known Carrera Unified Formulation. The results show that quasi-3D displacement and stress distribution are obtained using a set of tuning parameters to form adaptable strain shape functions that are optimized for the given structural problem.

Estimating the lateral speed of a fast shock driven by a coronal mass ejection at the location of solar radio emissions

Fast coronal mass ejections (CMEs) can drive shock waves capable of accelerating electrons to high energies. These shock-accelerated electrons act as sources of electromagnetic radiation, often in the form of solar radio bursts. Recent findings suggest that radio imaging of solar radio bursts can provide a means to estimate the lateral expansion of CMEs and associated shocks in the low corona. Our aim is to estimate the expansion speed of a CME-driven shock at the locations of radio emission using 3D reconstructions of the shock wave from multiple viewpoints. In this study, we estimated the 3D location of radio emission using radio imaging from the Nançay Radioheliograph and the 3D location of a CME-driven shock. The 3D shock was reconstructed using white-light and extreme ultraviolet images of the CME from the Solar Terrestrial Relations Observatory, Solar Dynamics Observatory, and the Solar and Heliospheric Observatory. The lateral expansion speed of the CME-driven shock at the electron acceleration locations was then estimated using the approximate 3D locations of the radio emission on the surface of the shock. The radio bursts associated with the CME were found to reside at the flank of the expanding CME-driven shock. We identified two prominent radio sources at two different locations and found that the lateral speed of the shock was between $ $ and $ second $ at these locations. Such a high speed during the early stages of the eruption already indicates the presence of a fast shock in the low corona. We also found a larger ratio between the radial and lateral expansion speed compared to values obtained higher up in the corona. We estimated for the first time the 3D expansion speed of a CME-driven shock at the location of the accompanying radio emission. The high shock speed obtained is indicative of a fast acceleration during the initial stage of the eruption. This acceleration leading to lateral speeds in the range of $ second $ is most likely one of the key parameters contributing to the presence of metric radio emissions, such as type II radio bursts.

Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning

Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications. To achieve this, most previous approaches formulate it as a problem of surface approximation and employ point-based networks to learn surface representations. However, learning surfaces from sparse point clouds is more challenging, and thus they often suffer from the low-fidelity geometry approximation. To address it, we propose an arbitrary-scale Point cloud Upsampling framework using Voxel-based Network (PU-VoxelNet). Thanks to the completeness and regularity inherited from the voxel representation, voxel-based networks are capable of providing predefined grid space to approximate 3D surface, and an arbitrary number of points can be reconstructed according to the predicted density distribution within each grid cell. However, we investigate the inaccurate grid sampling caused by imprecise density predictions. To address this issue, a density-guided grid resampling method is developed to generate high-fidelity points while effectively avoiding sampling outliers. Further, to improve the fine-grained details, we present an auxiliary training supervision to enforce the latent geometric consistency among local surface patches. Extensive experiments indicate the proposed approach outperforms the state-of-the-art approaches not only in terms of fixed upsampling rates but also for arbitrary-scale upsampling. The code is available at https://github.com/hikvision-research/3DVision

Piecewise Reconstruction of 3D Euler Spirals From Planar Polygonal Curves

In this article, we propose a method for reconstructing approximate piecewise 3D Euler spirals from planar polygonal curves. The method computes the 3D coordinates of approximate Euler spiral such that its orthogonal projection onto the 2D plane is the closest possible to the input curve. To achieve this, a dataset is created, comprising Euler spiral segments and their orthogonal projections. Given an input curve, it is sampled and split into segments. Each segment is matched with the closest Euler spiral segments from the dataset, forming a pool of candidates. The optimal set of connected Euler spiral segments is then selected to reconstruct the approximate piecewise 3D Euler spiral. The selection prioritizes smoothness continuity at connecting points while minimizing the distance between the orthogonal projection and the input curve. We evaluate our method against synthetic 3D Euler spirals by applying our reconstruction to the orthogonal projection of the synthetic Euler spirals.

A deep learning feed-forward neural network framework for the solutions to singularly perturbed delay differential equations

In this paper, we explore a deep learning feedforward artificial neural network (ANN) framework as a numerical tool for approximating the solutions to singularly perturbed delay differential equations (SPDDE). Our approach trains the network with fewer uniform data points along with linear interpolation as a dependent variable. More importantly, the exact solution is not used for training the network. A mean square or Euclidean norm type total loss function is utilized to assess the deep learning network’s performance. In the training and testing phase, our network adjusts itself to fit the tangent and the curvature by minimizing the total loss function and fine-tuning the network’s hyperparameters during the backpropagation stage. Our focus in this contribution is to investigate an answer to the question- “whether neural networks can learn to solve the differential equations with both delay and boundary layer behavior”? Traditional numerical methods are known to perform poorly in approximating the solutions to SPDDE due to boundary layer behavior, which is the sharp change in the gradient of the solution inside a region of very small width. Our proposed network architecture is used to investigate the above question for three varieties of SPDDE. The numerical results demonstrate that the fine-tuned adaptive deep learning architecture can effectively approximate the solution to SPDDE for varieties of delay and perturbation parameters. It is expected that the current method can be extendable to approximate 2D convection-diffusion partial differential equations with a boundary layer.

A Hermitian Cn finite cylindrical layer method for 3D size‐dependent buckling and free vibration analyses of simply supported FG piezoelectric cylindrical sandwich microshells subjected to axial compression and electric voltages

AbstractWithin the framework of the consistent couple stress theory (CCST), we develop a Hermitian Cn (n = 1, 2) finite cylindrical layer method (FCLM) for carrying out the three‐dimensional (3D) analysis of the size‐dependent buckling and free vibration behaviors of simply supported, functionally graded (FG) piezoelectric cylindrical sandwich microshells. The microshells of interest are placed under closed‐circuit surface conditions and subjected to axial compression and electric voltages. We derive a 3D weak formulation based on Hamilton's principle for this study. In the resulting formulation, the microshell is artificially divided into nl microlayers, with the elastic displacement components and the electric potential selected as the primary variables. By incorporating a layer‐wise kinematic model into our weak formulation, we develop a Hermitian Cn FCLM, which can be used for analyzing FG piezoelectric cylindrical sandwich microshells. Each primary variable is expanded as a double Fourier series in the in‐surface domain and is interpolated in the thickness direction using Hermitian Cn polynomials. The accuracy and the convergence rate of our Hermitian Cn FCLMs are validated by comparing the solutions they produce for FG piezoelectric cylindrical macroshells and FG elastic cylindrical microshells with the relevant exact and approximate 3D solutions which have been reported in the literature. The material length scale parameter of our FCLMs is set at zero in the comparison made with the FG piezoelectric macroshells. In contrast, the piezoelectric and flexoelectric effects are ignored in the comparison made with the FG elastic microshells. The impact of some essential factors on the critical load, critical voltage, and natural frequency of simply supported FG piezoelectric cylindrical sandwich microshells is assessed. The important factors are identified as piezoelectricity, flexoelectricity, the material length scale parameter, the inhomogeneity index, the radius‐to‐thickness ratio, the length‐to‐radius ratio, and the magnitude of the applied voltage and the applied load.

Ultra-precision grinding of light-weighted SiC aspheric mirror

Based on the approximate 3D model of light-weighted SiC aspheric mirror, the deformation and stress at different positions along the radius caused by the grinding force on the mirror surface was analysed. Then the influence of processing parameters on grinding force was experimentally studied, and the prediction model of grinding force was established. Finally, the grinding experiment of light-weighted SiC aspheric mirror was carried out. The PV value of the aspheric surface form error was 4.15μm, and the Ra of surface roughness was about 28nm. The experimental results verified the feasibility of ultra-precision forming of lightweight SiC mirror by controlling grinding force based on process parameters.

Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation

Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts.

Study of Multibranch Wells for Productivity Increase in Hydrate Reservoirs Based on a 3D Heterogeneous Geological Model: A Case in the Shenhu Area, South China Sea

Summary So far, a total of 11 hydrate trial production projects have been carried out all over the world, all of which used a single vertical well or horizontal well to carry out hydrate production by the depressurization method or depressurization combined with other methods. These traditional production methods have some limitations: The single vertical well has a small contact area with the reservoir, and the transmission range of the temperature and pressure is limited; therefore, the productivity is low. The horizontal well can improve hydrate productivity from magnitude order; however, there is a long distance from the standard of commercial production of marine hydrate. Therefore, it is an inevitable trend to find a highly efficient and advanced drilling technology for heterogeneous hydrate reservoirs. The multibranch wells based on horizontal wells can not only increase the contact area between the hydrate reservoir and well by branch structure to improve the conductivity of the reservoir temperature and pressure but also improve the hydrate productivity by laying the branch at high hydrate saturation for areas with extremely uneven distribution. Therefore, for this paper, we chose the Shenhu area as the research area to establish an approximate realistic 3D heterogeneous geological model contained with hydrate, then we laid the multibranch wells based on horizontal wells in high hydrate saturation area and optimized the branch direction, location, and spacing, and the production increasing effect was assessed. Finally, an optimal multibranch well scheme was obtained under the conditions of this paper setting, which is as follows: The vertical multibranch well was set at the root end of the horizontal main well with a branch spacing of 10 m, and the productivity after optimization was 31.64% higher than that before optimization.

Real-time approximate and combined 2D convolvers for FPGA-based image processing

Convolution widely has been used as the main part of the improvement in digital image processing applications. In convolutional computations, a large number of memory accesses and a huge amount of computations challenge its performance. Many of the related proposed convolvers are based on exact computations. Although exact convolvers keep the accuracy of the convolution operation at the top level, sometimes by missing a negligible amount of accuracy, the performance can be improved. Approximate computing is a new technique for solving computation overhead problems. In this paper, approximate 2D convolvers are presented which minimize the memory access rate and computations by a special factor of multiply-and-accumulate (MAC) terms. On the other hand, to preserve the flexibility for supporting different required accuracy, the proposed approximate convolvers are combined with the exact designs with real-time pre-processing stages by exploiting innovative methods which manage the hardware overhead. In comparison with conventional convolvers, the proposed designs improve the number of active resources which causes a significant reduction in power consumption. For 3 × 3 kernel size, the evaluation results on the Xilinx Virtex-7 (XC7V2000t) FPGA device show 34% and 20% power optimization of the proposed approximate and combined convolvers, respectively, in comparison with exact convolver (EC). Also, this improvement grows by increasing the kernel size. Finally, a comparison based on RMSE and PSNR for different sample images and filters reveals that the error rate and image quality reduction are acceptable for many real-time image processing applications.

3D static bending analysis of functionally graded piezoelectric microplates resting on an elastic medium subjected to electro-mechanical loads using a size-dependent Hermitian C 2 finite layer method based on the consistent couple stress theory

Based on the consistent couple stress theory (CCST), we develop a size-dependent Hermitian C 2 finite layer method (FLM) for carrying out the three-dimensional (3D) static bending analysis of a simply-supported, functionally graded (FG) piezoelectric microplate which is placed under closed-circuit surface conditions. The microplate of interest is assumed to be resting on a Winkler-Pasternak foundation and subjected to either sinusoidal or uniformly distributed electro-mechanical loads. By setting the material length scale parameter at zero and ignoring the piezoelectric and flexoelectric effects, we reduce the formulation of the Hermitian C 2 FLM for analyzing FG piezoelectric microplates to that for analyzing FG piezoelectric macroplates and FG elastic microplates, respectively. The accuracy and the convergence rate of the Hermitian C 2 FLM are assessed by comparing the solutions it produces with the exact and approximate 3D solutions of FG piezoelectric macroplates and FG elastic microplates reported in the literature. Because the Hermitian C 2 FLM requires that the first-order and second-order derivatives of the primary variables must be continuous at each nodal plane, which in turn leads to their solutions converging rapidly and being able to obtain the accurate results of the electric and elastic variables induced in the microplates, especially for the transverse shear and normal stresses and the electric displacements. The effects of piezoelectricity, flexoelectricity, and the material length scale parameter on the deformations and stresses induced in the FG piezoelectric microplates are significant. Highlights A CCST-based Hermitian C 2 finite layer method (FLM) is developed for analyzing functionally graded (FG) piezoelectric microplates. The current FLM can be reduced to that for analyzing FG elastic microplates by ignoring the piezoelectric and flexoelectric effects. The current FLM can be reduced to that for analyzing FG piezoelectric macroplates by setting the material length scale parameter at zero. Implementing the current FLM reveals that its solutions converge rapidly and are in excellent agreement with those obtained using the exact 3D models. The effects of piezoelectricity, flexoelectricity, and the material length scale parameter on the static bending behavior of FG piezoelectric microplates are significant.

Evaluation of 1D Models for Particle Transport with Wall Migration in Ducts of Rectangular Cross Section

Approximate 1D models of particle transport in ducts that make use of an exponential displacement kernel to describe the effect of wall migration are evaluated for ducts of rectangular cross section. The studied models are based on the use of either one or two basis functions to approximate the transverse and azimuthal dependencies of the angular flux in the duct. Thermal-neutron reflection and transmission probabilities resulting from Monte Carlo simulations carried out with the MCNP and PHITS codes for iron, concrete and graphite ducts are used as reference solutions. It is concluded that the model built on two basis functions performs well (deviations typically < 10% in magnitude) for iron, the material for which absorption is most important, but less so for the scattering-dominated materials concrete and graphite. It is also concluded that changes in the energy distribution of the incident source have their strongest influence on the results for iron ducts.

A Size-Dependent Finite Element Method for the 3D Free Vibration Analysis of Functionally Graded Graphene Platelets-Reinforced Composite Cylindrical Microshells Based on the Consistent Couple Stress Theory

Within a framework of the consistent couple stress theory (CCST), a size-dependent finite element method (FEM) is developed. The three-dimensional (3D) free vibration characteristics of simply-supported, functionally graded (FG) graphene platelets (GPLs)-reinforced composite (GPLRC) cylindrical microshells are analyzed. In the formulation, the microshells are artificially divided into numerous finite microlayers. Fourier functions and Hermitian C2 polynomials are used to interpolate the in-surface and out-of-surface variations in the displacement components induced in each microlayer. As a result, the second-order derivative continuity conditions for the displacement components at each nodal surface are satisfied. Five distribution patterns of GPLs varying in the thickness direction are considered, including uniform distribution (UD) and FG A-type, O-type, V-type, and X-type distributions. The accuracy and convergence of the CCST-based FEM are validated by comparing the solutions it produces with the exact and approximate 3D solutions for FG cylindrical macroshells reported in the literature, for which the material length scale parameter is set at zero. Numerical results show that by increasing the weight fraction of GPLs by 1%, the natural frequency of FG-GPLRC cylindrical microshells can be increased to more than twice that of the homogeneous cylindrical microshells. In addition, the effects of the material length scale parameter, the GPL distribution patterns, and the length-to-thickness ratio of GPLs on natural frequencies of the FG-GPLRC cylindrical microshells are significant.

MeTACAST: Target- and Context-Aware Spatial Selection in VR.

We propose three novel spatial data selection techniques for particle data in VR visualization environments. They are designed to be target- and context-aware and be suitable for a wide range of data features and complex scenarios. Each technique is designed to be adjusted to particular selection intents: the selection of consecutive dense regions, the selection of filament-like structures, and the selection of clusters-with all of them facilitating post-selection threshold adjustment. These techniques allow users to precisely select those regions of space for further exploration-with simple and approximate 3D pointing, brushing, or drawing input-using flexible point- or path-based input and without being limited by 3D occlusions, non-homogeneous feature density, or complex data shapes. These new techniques are evaluated in a controlled experiment and compared with the Baseline method, a region-based 3D painting selection. Our results indicate that our techniques are effective in handling a wide range of scenarios and allow users to select data based on their comprehension of crucial features. Furthermore, we analyze the attributes, requirements, and strategies of our spatial selection methods and compare them with existing state-of-the-art selection methods to handle diverse data features and situations. Based on this analysis we provide guidelines for choosing the most suitable 3D spatial selection techniques based on the interaction environment, the given data characteristics, or the need for interactive post-selection threshold adjustment.

Novel Airborne EM Image Appraisal Tool for Imperfect Forward Modeling

Full 3D inversion of time-domain Airborne ElectroMagnetic (AEM) data requires specialists’ expertise and a tremendous amount of computational resources, not readily available to everyone. Consequently, quasi-2D/3D inversion methods are prevailing, using a much faster but approximate (1D) forward model. We propose an appraisal tool that indicates zones in the inversion model that are not in agreement with the multidimensional data and therefore, should not be interpreted quantitatively. The image appraisal relies on multidimensional forward modeling to compute a so-called normalized gradient. Large values in that gradient indicate model parameters that do not fit the true multidimensionality of the observed data well and should not be interpreted quantitatively. An alternative approach is proposed to account for imperfect forward modeling, such that the appraisal tool is computationally inexpensive. The method is demonstrated on an AEM survey in a salinization context, revealing possible problematic zones in the estimated fresh–saltwater interface.

Orion A’s complete 3D magnetic field morphology

Magnetic fields permeate the interstellar medium and are important in the star formation process. Determining the three-dimensional (3D) magnetic fields of molecular clouds will allow us to better understand their role in the evolution of these clouds and the formation of stars. We fully reconstruct the approximate 3D magnetic field morphology of the Orion A molecular cloud (on scales of a few to ∼100 pc) using Galactic magnetic field models, as well as available line-of-sight and plane-of-sky magnetic field observations. While previous studies identified the 3D magnetic field morphology of the Orion A cloud as an arc shape, in this study we provide the orientation of this arc-shaped field and its plane-of-sky direction for the first time. We find that this 3D field is a tilted, semi-convex (from our point of view) structure and mostly points in the direction of decreasing latitude and longitude on the plane of the sky from our vantage point. The previously identified bubbles and events in this region were key in shaping this arc-shaped magnetic field morphology.

Approximate TSV-based 3D Stacked Integrated Circuits by Inexact Interconnects

Three-Dimensional Stacked Integrated Circuit (3D-SICs) based on Through-Silicon Vias (TSVs) provide a high-density integration technology. However, integrating pre-tested dies requires post-bond interconnect testing, which is complex and costly. An imperfect TSV-based interconnect indicates a defective chip that should be rejected. Thus, it increases the yield loss and test cost. On the other hand, approximate computing (AC) is a promising design paradigm suitable for error-resilient applications, e.g., processing sensory-generated data, by judiciously sacrificing output accuracy. AC perform inexact operations and accepts inexact data. Thus, introducing AC into 3D-SICs will significantly ameliorate the efficiency of design approximation. Therefore, this work aims to increase the yield and reduce the test cost by accepting 3D-SICs with defected interconnects as approximate 3D-SICs. This work considers 3D-SICs, where the sensor is stacked on logic (CPU) which is stacked on memory (DRAM). Then, use the memory-based interconnect testing (MBIT) approach to detect and diagnose the faulty interconnect. Based on the detected fault location and type, and for a maximum allowed error, some sensory 3D-SICs with defected LSBs interconnects are accepted and used in error-resilient and data-intensive applications. Targeting data lines only, 50% of the defected interconnects, i.e., least significant bits (LSBs), were accepted as approximate. Thus, the proposed work was able to significantly increase the yield. Two applications, i.e., ECG signal compression and detecting of their R peaks,demonstrated the effectiveness of using a sensory device with a faulty data line in its least significant 8-bits. The approximate ECG signals have a compression rate higher than the exact with negligible (around 0.1%) reduced accuracy.