Variable Resolution Research Articles

Advancing Soil Organic Carbon and Total Nitrogen Modelling in Peatlands: The Impact of Environmental Variable Resolution and vis-NIR Spectroscopy Integration

Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and total nitrogen (TN) across landscapes. However, the impact of combining vis-NIR spectroscopy with high-resolution RS data for SOC and TN prediction remains an open question. This study evaluated the effects of incorporating a high-resolution LiDAR-derived digital elevation model (DEM) and a medium-resolution SRTM-derived DEM with vis-NIR spectroscopy for predicting SOC and TN in peatlands. A total of 57 soil cores, comprising 262 samples from various horizons (<2 m), were collected and analysed for SOC and TN content using traditional methods and ASD Fieldspec® 4. The 262 observations, along with elevation data from LiDAR and SRTM, were divided into 80% training and 20% testing datasets. By employing the Cubist modelling approach, the results demonstrated that incorporating high-resolution LiDAR data with vis-NIR spectra improved predictions of SOC (RMSE: 4.60%, RPIQ: 9.00) and TN (RMSE: 3.06 g kg−1, RPIQ: 7.05). In conclusion, the integration of LiDAR and soil spectroscopy holds significant potential for enhancing soil mapping and promoting sustainable soil management.

Atmospheric Circulation Patterns Associated with Extreme Wind Events in Canadian Cities

Abstract Extreme near-surface wind speeds in cities can have major societal impacts but are not well represented in climate models. Despite this, large-scale dynamics in the free troposphere, which models resolve better, could provide reliable constraints on local extreme winds. This study identifies synoptic circulations associated with midlatitude extreme wind events and assesses how resolution affects their representation in analysis products and a climate model framework. Composites of reanalysis (ERA5) sea level pressure and upper-tropospheric winds during observed extreme wind events reveal distinct circulation structures for each quadrant of the surface-wind rose. Enhanced resolution of the analysis product (ERA5 versus the higher-resolution ECMWF Operational Analysis) reduced wind speed biases but has little impact on capturing occurrences of wind extremes seen in station observations. Composite circulations for surface wind extremes in a climate model (CESM) skillfully reproduce circulations found in reanalysis. Regional refinement of CESM over a region centered on southern Ontario, Canada, using variable resolution (VR-CESM) improves representation of surface ageostrophic circulations and the strength of vertical coupling between upper-level and near-surface winds. We thus can distinguish situations for which regional refinement (dynamical downscaling) is necessary for realistic representation of the large-scale atmospheric circulations associated with extreme winds, from situations where the coarse resolution of standard GCMs is sufficient. Significance Statement In this study we identify the large-scale atmospheric circulation patterns that drive extreme wind speeds in Canadian cities, and how well numerical climate models, which are used for producing climate change projections, represent these circulation patterns. Climate models do not simulate local winds as accurately as larger-scale phenomena, so this work can help identify useful information that models contain regarding extreme winds. For cities in eastern Canada, a benchmark model generally performs well, but a model with refined spatial resolution over southern Ontario improves agreement with patterns for observed extreme winds in that region.

LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources

Abstract. We present the Land Input Generator (LandInG) version 1.0, a new toolbox for generating input datasets for terrestrial ecosystem models (TEMs) from diverse and partially conflicting data sources. While LandInG 1.0 is applicable to process data for any TEM, it is developed specifically for the open-source dynamic global vegetation, hydrology, and crop growth model LPJmL (Lund–Potsdam–Jena with managed Land). The toolbox documents the sources and processing of data to model inputs and allows for easy changes to the spatial resolution. It is designed to make inconsistencies between different sources of data transparent so that users can make their own decisions on how to resolve these should they not be content with the default assumptions made here. As an example, we use the toolbox to create input datasets at 5 and 30 arcmin spatial resolution covering land, country, and region masks, soil, river networks, freshwater reservoirs, irrigation water distribution networks, crop-specific annual land use, fertilizer, and manure application. We focus on the toolbox describing the data processing rather than only publishing the datasets as users may want to make different choices for reconciling inconsistencies, aggregation, spatial extent, or similar. Also, new data sources or new versions of existing data become available continuously, and the toolbox approach allows for incorporating new data to stay up to date.

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

An input signal dependent 8-to-12 bit variable resolution SAR ADC with digitally implemented bit enhancement Logic

This paper presents variable resolution SAR ADC with digitally implemented Bit enhancement logic. Unlike conventional approaches, this work avoids complex hardware architecture to reduce power consumption as the variable resolution is achieved using digitally implemented VRL (Variable Resolution Logic) block instead of combining different ADC configurations altogether. The proposed architecture utilizes the VRL block to enhance the resolution. This work first uses the linear range of an 8-bit SAR ADC as a learning dataset and then uses corresponding output bits as training data for VRL block. This VRL block further enhances the resolution with the help of a 2-bit voltage-to-digital converter by self-adjusting the required resolution after estimating the error. Less variation in sampling rate from 250 MHz to 175 MHz is achieved after implementing the VRL block. The proposed architecture is designed in 180 nm CMOS technology to validate the work. The simulation results show the ADC achieves an SNDR of 47.4 dB for 8-bit and increases to 72.11 dB for 12-bit resolution. The FoM of 253 fJ/conversion step for 8-bit SAR ADC is achieved, and it ranges from 168.3 to 35.7 fJ/conversion step in Variable resolution block phase. The area of the proposed ADC is 315.86 × 151.30 µm2.

Combining Satellite-Derived PM2.5 Data and a Reduced-Form Air Quality Model to Support Air Quality Analysis in US Cities.

Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra-urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city-scale decision-making. To reduce InMAP's biases and increase its relevancy for urban-scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite-derived speciated PM2.5 from Washington University and ground-level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground-monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: -48%, pNO3: 8%, pNH4: 69%), but with city-specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city-scaling approach (15%-27%). The city-specific scaling method also improves the R 2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36-0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non-EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide -6%).

Rapid decline of carbon monoxide emissions in the Fenwei Plain in China during the three-year Action Plan on defending the blue sky

The Fenwei Plain is one of China's most polluted regions, with poor atmospheric dispersion conditions and an outdated energy structure. After implementing multiple policies in recent years, significant reductions in air pollutant concentrations were observed. In this study, based on the Lagrangian-Bayesian inversion framework FLEXINVERT, we constructed a variable resolution inversion system focusing on the Fenwei Plain and inferred the carbon monoxide (CO) emissions using in-situ atmospheric CO observations from April 2014 to March 2020. We analyzed the spatiotemporal variations of the CO emissions and discussed their causes, especially the effect of the “Three-year Action Plan on Defending the Blue Sky” (TAPDBS). Before the policy, CO emissions temporarily increased, and the overall decrease in CO emissions per unit of Gross Domestic Product (GDP) slowed down. When the policy was implemented, CO emission fluxes declined sharply, with an average drop of 28%, accompanied by an even higher 37% decrease of CO emission per GDP. The reasons for the decline in CO emissions in Shanxi, Shaanxi and Henan are diverse. The decrease in energy intensity is the reason for CO emission reduction in Shannxi and Henan province but not in Shanxi province. This research fills the gap in emission information in recent years and confirms that TAPDBS has brought a breakthrough in both economic development and air quality protection in the Fenwei Plain.

THE CURVE ARC AS A STRUCTURE ELEMENT OF AN OBJECT CONTOUR IN THE IMAGE TO BE RECOGNIZED

Context. The proposed article relates to the field of visual information processing in a computer environment, more precisely to the determination the parameters of the interest object in the image, in particular, the contour of the interest object In most cases, the contour of an object is a simply connected sequence of curve arcs.&#x0D; Objective. The purpose and subject of the study is to find and to propose such a definition of the digital curve arc, as the most important element of the object contour in the recognizable image, which does not contradict modern neurophysiological conceptions about visual perception, and to recognize the object contour as a sequence of the digital curve arcs.&#x0D; Method. The representation of the image in the form of a structural model is used, one of the structural elements of which is the contour of the object, consisting of digital curve arcs. Also, the image is considered as a cellular complex which corresponds to modern ideas about human visual perception.&#x0D; Results. The new definition for arc of a digital curve as a sequence of digital straight segments is proposed, which does not contradict to modern concepts of neurophysiology. In contrast to the known definitions of a curve arc, the proposed definition of a digital curve arc makes it possible to determine the start and end points of the arc. According to the description of the contour of an object as a simply connected closed sequence of line segments, it is proposed to construct a description of the contour as a sequence of arcs of digital curves.&#x0D; Conclusions. The use of the proposed definition of the digital curve arc in image processing makes it possible to recognize the contour of an object in an image and present it in a form close to visual perception. For best results, the use of variable resolution in image processing algorithms is recommended.

The use of different 16S rRNA gene variable regions in biogeographical studies.

16S rRNA gene amplicon sequencing is routinely used in environmental surveys to identify microbial diversity and composition of the samples of interest. The dominant sequencing technology of the past decade (Illumina) is based on the sequencing of 16S rRNA hypervariable regions. Online sequence data repositories, which represent an invaluable resource for investigating microbial distributional patterns across spatial, environmental or temporal scales, contain amplicon datasets from diverse 16S rRNA gene variable regions. However, the utility of these sequence datasets is potentially reduced by the use of different 16S rRNA gene amplified regions. By comparing 10 Antarctic soil samples sequenced for five different 16S rRNA amplicons, we explore whether sequence data derived from diverse 16S rRNA variable regions can be validly used as a resource for biogeographical studies. Patterns of shared and unique taxa differed among samples as a result of variable taxonomic resolutions of the assessed 16S rRNA variable regions. However, our analyses also suggest that the use of multi-primer datasets for biogeographical studies of the domain Bacteria is a valid approach to explore bacterial biogeographical patterns due to the preservation of bacterial taxonomic and diversity patterns across different variable region datasets. We deem composite datasets useful for biogeographical studies.

Using LASSO regularization to project recruitment under CMIP6 climate scenarios in a coastal fishery with spatial oceanographic gradients

As climate change disrupts fisheries, scientists are interested in fisheries projections under climate change scenarios. However, projections that account for spatial oceanographic gradients use increased variable selection power and output high spatial resolution climate data are needed to improve strategic fisheries management. This study uses the least absolute squares and selection operator, a regularization technique, and improved, climate change projections from phase 6 of the Couple Model Intercomparison Project to relate Atlantic surfclam, Spisula solidissima solidissima, recruitment to climate variables. Results show a longitudinal gradient in New York State waters where western recruitment displays a negative relationship with sea surface temperature and eastern recruitment displays a negative relationship with eastward spring wind intensity. Models project that recruitment in 2050 will decrease 100% in western waters and remain sporadic, but high, in eastern waters. This study provides insight regarding surfclam responses to climate change and considerations (methodological and statistical) for improved climate-based fisheries projections.

Fluctuation Electron Microscopy on Amorphous Silicon and Amorphous Germanium.

Variable resolution fluctuation electron microscopy experiments were performed on self-ion implanted amorphous silicon and amorphous germanium to analyze the medium-range order. The results highlight that the commonly used pair-persistence analysis is influenced by the experimental conditions. Precisely, the structural correlation length Λ, a metric for the medium-range order length scale in the material, obtained from this particular evaluation varies depending on whether energy filtering is used to acquire the data. In addition, Λ depends on the sample thickness. Both observations can be explained by the fact that the pair-persistence analysis utilizes the experimentally susceptible absolute value of the normalized variance obtained from fluctuation electron microscopy data. Instead, plotting the normalized variance peak magnitude over the electron beam size offers more robust results. This evaluation yields medium-range order with an extent of approximately (1.50 ± 0.50) nm for the analyzed amorphous germanium and around (1.10 ± 0.20) nm for amorphous silicon.

Fast, Accurate, and System-Specific Variable-Resolution Modeling of Proteins.

In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.

Tropical cyclone strength, precipitation, and environment in variable resolution CAM-MPAS simulations over Western North Pacific

Tropical cyclone strength, precipitation, and environment in variable resolution CAM-MPAS simulations over Western North Pacific

Enabling Variable High Spatial Resolution Retrieval From a Long Pulse BOTDA Sensor

Spatial resolution (SR) is one of the most important parameters of Brillouin optical time-domain analysis (BOTDA) sensors, which determines the minimum length that a perturbation event can be distinguished. In the field of Internet of Things (IoT), there is an urgent need for sensors with large-scale high-precision sensing capability for scenarios, such as intelligent monitoring of production lines and urban infrastructure. Conventionally, the SR is normally restricted to be longer than 1 m due to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 10$ </tex-math></inline-formula> -ns acoustic lifetime limitation in silica optical fibers. For long-distance smart monitoring systems, the SR is generally on the order of several meters or even worse. However, it does not meet the needs of many applications. Therefore, there is an urgent need to achieve SR in the submeter magnitude. In this work, for the first time to the best of our knowledge, we propose a convolutional neural network (CNN) to process the data of conventional BOTDA sensors, which achieves unprecedented performance improvement that allows to directly retrieve submeter SR from the sensing system that use long pump pulses. By using the simulated Brillouin gain spectrums (BGSs) as the CNN input and the corresponding high SR Brillouin frequency shift (BFS) as the output target, the trained CNN is able to obtain an SR higher than the theoretical value determined by the pump pulse width. In the experiment, the CNN accurately retrieves 0.5-m hotspots from the measured BGS with pump pulses from 20 to 50 ns, and the acquired BFS is in great agreement with 45/40 ns differential pulse-width pair (DPP) measurement results. Compared with the DPP technique, the proposed CNN demonstrates a twofold improvement in BFS uncertainty with only half the measurement time. In addition, by changing the training data sets, the proposed CNN can obtain tunable high SR retrieval based on conventional BOTDA sensors that use long pulses without any requirement of hardware modifications. It is worth mentioning that the proposed method is also applicable to larger pulse widths to retrieve a submeter SR. The proposed data post-processing approach paves the way to enable novel high SR BOTDA sensors, which brings substantial improvement over the state-of-the-art techniques in terms of system complexity, measurement time, reliability, etc.

Endocast, brain, and bones: Correspondences and spatial relationships in squamates.

Vertebrate endocasts are widely used in the fields of paleoneurology and comparative neuroanatomy. The validity of endocranial studies is dependent upon the extent to which an endocast reflects brain morphology. Due to the variable neuroanatomical resolution of vertebrate endocasts, direct information about the brain morphology can be sometimes difficult to assess and needs to be investigated across lineages. Here, we employ X-ray computed tomography (CT), including diffusible iodine-based contrast-enhanced CT, to qualitatively compare brains and endocasts in different species of squamates. The relative position of the squamate brain within the skull, as well as the variability that may exist in such spatial relationships, was examined to help clarify the neurological regions evidence on their endocasts. Our results indicate that squamate endocasts provide variable representation of the brain, depending on species and neuroanatomical regions. The olfactory bulbs and peduncles, cerebral hemispheres, as well as the medulla oblongata represent the most easily discernable brain regions from squamate endocasts. In contrast, the position of the optic lobes, the ventral diencephalon and the pituitary may be difficult to determine depending on species. Finally, squamate endocasts provide very limited or no information about the cerebellum. The spatial relationships revealed here between the brain and the surrounding bones may help to identify each of the endocranial region. However, as one-to-one correspondences between a bone and a specific region appear limited, the exact delimitation of these regions may remain challenging according to species. This study provides a basis for further examination and interpretation of squamate endocast disparity.

Irrigation management zone delineation and optimal irrigation scheduling for center pivot irrigation systems

In this study, a three-stage irrigation management zone delineation approach and a model-based scheduler are proposed to provide variable rate irrigation schedules for a 26.4-hectare field at the Alberta Irrigation Technology Centre in Lethbridge, Alberta, Canada. After an initial demarcation of the investigated field into quadrants according to the four different crops growing in the field, the k-means clustering approach is used to delineate each quadrant further into smaller irrigation management zones on the basis of soil hydraulic parameters, elevation of the field and the variable rate irrigation resolution of the center pivot system. For each management zone, a long short-term memory (LSTM) neural network model is then developed to characterize the soil water dynamics in the root zone. Based on the LSTM models, and based on the LSTM models, a mixed-integer model predictive controller (MPC) is designed to prescribe irrigation schedules for the various management zones in each quadrant to ensure optimal crop water uptake while minimizing water consumption and irrigation costs. The applicability of the proposed approach is illustrated in simulations. The results show that the proposed approach achieves 26% water savings compared to a triggered scheduling scheme.

Adaptive Clustering-Based Method for ICESat-2 Sea Ice Retrieval

The great potential of NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) to retrieve sea ice heights has been demonstrated. However, the presence of a significant number of noise photons in the ICESat-2 data makes accurate monitoring of sea ice changes challenging. This paper proposes an adaptive clustering and kernel density estimation-based (AC-KDE) method for estimating sea ice heights in ICESat-2 photon clouds. First, the adaptive clustering method effectively detects sea ice signal photons. The method’s input parameters are determined based on the ATLAS parameters and the LiDAR transmission equation. Then, the adaptive-count signal photon aggregates are used to estimate sea ice heights, and a variable along-track resolution is obtained using the kernel density estimation method. The AC-KDE method is applied to the MABEL and ICESat-2 data, and we compare it with other denoising algorithms, including the HBM, DBSCAN, OPTICS, UMD_RDA, DDM, and ILSM algorithms. The results indicate that the proposed method outperforms these algorithms in extracting signal photons with higher accuracy scores and F-scores, which are 0.97 & 0.97, 0.92 & 0.90, and 0.89 & 0.72 under high-medium-low signal-to-noise ratio conditions, respectively. Additionally, the retrieved sea ice heights are compared with the ATL07 heights. The AC-KDE heights show a significant correlation with coincident ATM heights, and have a lower RMSE value (0.066 m) compared to ATL07 heights (0.104 m). The AC-KDE method also demonstrates a vertical height precision of 0.01 m over flat leads. The proposed method can effectively extract signal photons and accurately estimate sea ice heights in polar regions.

Spatial Variable Resolution Scanning Three-Dimensional Reconstruction Imaging

Spatial Variable Resolution Scanning Three-Dimensional Reconstruction Imaging

障害物の角速度情報と解像度可変ポテンシャル場を用いた自律走行パーソナルモビリティの運動制御

In recent years, personal mobility (PM), electric vehicles that are smaller and slower than conventional automobiles, have been attracting attention, and are expected to be used on sidewalks and in shared spaces where vehicles and pedestrians can coexist. PM is also expected to be used as a means of transportation for the physically challenged and the elderly who have difficulty getting around by themselves, and research is being conducted to automate PM operation. In this situation, our group proposed a PM motion control method based on a spatio-temporal potential that takes into account the motion characteristics of obstacles. However, this method is not sufficient to avoid obstacles with large angular velocities, such as pedestrians. It is also not a practical method because of the time required for computation. Therefore, this paper proposes a new practical PM motion control method that improves these points. By defining a new potential field that takes into account the angular velocity of the obstacle, the proposed method improves the performance against obstacles whose avoidance performance was insufficient in the previous method. At the same time, to increase practicality, a reduction algorithm with variable resolution of the potential field is incorporated as a method to reduce computational complexity while maintaining avoidance performance as much as possible. Finally, the effectiveness of the proposed method is verified through numerical simulations assuming a shared space.

Modelling of Depth Prediction Algorithm for Intra Prediction Complexity Reduction

Video compression gained its relevance with the boon of the internet, mobile phones, variable resolution acquisition device etc. The redundant information is explored in initial stages of compression that’s is prediction. Inter prediction that is prediction within the frame generates high computational complexity when working with traditional signal processing procedures. The paper proposes the design of a deep convolutional neural network model to perform inter prediction by crossing out the flaws in the traditional method. It briefs the modeling of network, mathematics behind each stage and evaluation of the proposed model with sample dataset. The video frame’s coding tree unit (CTU) of 64x64 is the input, the model converts and store it as a 16-element vector with the goodness of CNN network. It gives an overview of deep depth decision algorithm. The evaluation process shows that the model performs better for compression with less computational complexity.