Full Area Coverage Research Articles

STAR-RIS-aided NOMA communication for mobile edge computing using hybrid deep reinforcement learning

STAR-RIS-aided NOMA communication for mobile edge computing using hybrid deep reinforcement learning

Two-dimensional kinematics and dynamical modelling of the ‘Jackpot’ gravitational lens from deep MUSE observations

ABSTRACT We present results from the first spatially resolved kinematic and dynamical modelling analysis of the unique SDSSJ0946+1006 (‘Jackpot’) triple-source lens system, where a single massive foreground $z\, =\, 0.222$ galaxy multiple-images three background sources at different redshifts. Deep integral field unit spectroscopic data were obtained using the MUSE instrument on the VLT, which, compared to previous single-slit observations, provides full azimuthal area coverage, high sensitivity (5 h integration) and high angular resolution (0.5 arcsec full width at half-maximum). To account for the strong continuum contributions from the $z\, =\, 0.609$ source, a multiple-component stellar template fitting technique is adopted to fit to the spectra of both the lens galaxy and the bright lensed background arc simultaneously. Through this, we robustly measure the first and second moments of the 2D stellar kinematics out to about 10 kpc from the centre of the lens, as well as resolving the inner profile inwards to ∼1 kpc. The 2D kinematic maps show a steep velocity dispersion gradient and a clear rotational component. We constrain the characteristic properties of the stellar and dark matter (DM) mass components with a sufficiently flexible parametrised dynamical model and an imposed lensing mass and find a DM density slope of $\gamma \, =\, 1.73\substack{+0.17 \\ -0.26}$, i.e. significantly steeper than an unmodified NFW profile ($\gamma \, =\, 1$) and consistent with a contracted DM halo. Our fitted models have a lensing-equivalent density slope of $\eta \, =\, 0.96\pm 0.02$, and thus we confirm most pure lensing results in finding a near isothermal profile for this galaxy.

Laser Voltage Probing Attack Detection With 100% Area/Time Coverage at Above/Below the Bandgap Wavelength and Fully-Automated Design

In this work, a detection scheme signaling the occurrence of laser voltage probing (LVP) attacks in digital designs (e.g., cryptographic circuits) is introduced. The scheme comprises a standard cell-based photosensor fabric and distributed detectors. By construction, the scheme conforms the standard cell design discipline, restricted layout design rules, and commercial place and route tools. This allows seamless integration with automated design flows, and immediate adoption in a design-agnostic fashion. Hundred percentage time and area coverage is achieved for full protection against LVP attacks, while not requiring any calibration. Extensive attacks with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $</tex-math> </inline-formula> 150 h of characterization with a state-of-the-art LVP equipment on a 28-nm testchip demonstrate laser detection at both above-and below-bandgap wavelengths and, hence, even at the very low electron-hole pair generation rate of the former ones. Attacks to an advanced encryption standard (AES) core are shown to be flagged at any practical laser intensity and best-in-class true 220-nm laser spot. Low circuit activity confines its power to leakage, amounting to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 0.1% of the AES power. At the worst case, 150% area overhead is substantially lower in practical cases where the information-sensitive blocks are a portion of it (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 20% in an advanced reduced instruction set computer machines (ARM) Cortex-M4 processor with a secure AES accelerator). To the best of the authors’ knowledge, this is the first demonstration of a design-agnostic on-chip protection against LVP attacks with full-area coverage under unmodified manufacturing/packaging process.

AUV planning and calibration method considering concealment in uncertain environments

IntroductionAutonomous underwater vehicles (AUVs) are required to thoroughly scan designated areas during underwater missions. They typically follow a zig-zag trajectory to achieve full coverage. However, effective coverage can be challenging in complex environments due to the accumulation and drift of navigation errors. Possible solutions include surfacing for satellite positioning or underwater acoustic positioning using transponders on other vehicles. Nevertheless, surfacing or active acoustics can compromise stealth during reconnaissance missions in hostile areas by revealing the vehicle’s location.MethodsWe propose calibration and planning strategies based on error models and acoustic positioning to address this challenge. Acoustic markers are deployed via surface ships to minimize navigation errors while maintaining stealth. And a new path planning method using a traceless Kalman filter and acoustic localization is proposed to achieve full-area coverage of AUVs. By analyzing the statistics of accumulated sensor errors, we optimize the positions of acoustic markers to communicate with AUVs and achieve better coverage. AUV trajectory concealment is achieved during detection by randomizing the USV navigation trajectory and irregularizing the locations of acoustic marker.ResultsThe proposed method enables the cumulative determination of the absolute position of a target with low localization error in a side-scan sonar-based search task. Simulations based on large-scale maps demonstrate the effectiveness and robustness of the proposed algorithm.DiscussionSolving the problem of accumulating underwater localization errors based on inertial navigation by error modeling and acoustic calibration is a typical way. In this paper, we have implemented a method to solve the localization error in a search scenario where stealth is considered.

Optimization of coverage path planning algorithm for robots

Abstract. In the era of technology gradually into the home, sweeping robots have become a must for many families. However, due to the lack of algorithms in the robot itself, there are still many problems with sweeping robots. In order to save cost and improve efficiency, it is necessary to improve the algorithm of robot coverage path planning. In this paper, we study the robot to complete full-area coverage path planning using sensors in an unknown environment. In this paper, the wavefront algorithm is combined with the traditional zigzag full-area path planning algorithm. The authors find that the combined algorithm speeds up the sweeping and reduces the repetition rate. Combining several different algorithms together like this can have more different applications in real life.

Experimental Investigation for the Phase Change Material Barrier Area Effect on the Thermal Runaway Propagation Prevention of Cell-to-Pack Batteries

Thermal runaway propagation (TRP) is a primary safety issue in lithium-ion battery (LIB) applications, and the use of a thermal barrier is considered to be a promising solution for TRP prevention. However, the operating conditions of the battery are extremely complicated, such as fast charging, low-temperature heating and thermal runaway. To date, there is no consistent answer as to how to choose the appropriate thermal barrier for such a complicated working environment. In this study, the characteristics of hydrogel based on sodium polyacrylate are explored, and the impact of thermal barrier area on TRP is investigated through experiments. Due to the prismatic battery structure, thermal barriers placed between cells are designed with different areas (148 × 98 mm, 128 × 88 mm, and 108 × 78 mm). The results indicate that test 1 without a placed thermal barrier quickly completes the TRP process, and the thermal runaway (TR) behavior is more violent. With a thermal barrier that does not have full area coverage placed between cells (test 2 and test 3), the propagation time is prolonged, but TRP still occurs. Compared with test 1, the triggered temperature of T2 F (the front surface of cell 2) is reduced by 207.6 °C and 295.2 °C, respectively. The complete area coverage thermal barrier successfully prevents TRP, and the T2 F of cell 2 only reaches 145.4 °C under the phase change by the hydrogel. This study may suggest a safety design for battery modules and prevent propagation among batteries.

Extensive protected area coverage and an updated global population estimate for the Endangered Madagascar Serpent-eagleEutriorchis astur

SummaryKnowledge gaps regarding distribution, habitat associations, and population size for rare and threatened range-restricted taxa lead to uncertainty in directing conservation action. Quantifying range metrics and species–habitat associations using Species Distribution Models (SDMs) with remote-sensing habitat data can overcome these setbacks by establishing baseline estimates for biological parameters critical for conservation assessments. Area of Habitat (AOH) is a new range metric recently developed by the International Union for Conservation of Nature (IUCN) Red List. AOH seeks to quantify inferred habitat within a species’ range to inform extinction risk assessments. Here, we used SDMs correlating occurrences with remote-sensing covariates to calculate a first estimate of AOH for the Endangered Madagascar Serpent-eagleEutriorchis astur, and then updated additional IUCN range metrics and the current global population estimate. From these baselines we then conducted a gap analysis assessing protected area coverage. Our continuous SDM had robust predictive performance (Continuous Boyce Index = 0.835) and when reclassified to a binary model estimated an AOH = 30,121 km2, 13% less than the current IUCN range map. We estimated a global population of 533 mature individuals derived from the Madagascar Serpent-eagle AOH metric, which was within current IUCN population estimates. The current protected area network covered 95% of AOH, with the binary model identifying three additional key habitat areas as new protected area designations to fully protect Madagascar Serpent-eagle habitat. Our results demonstrated that correlating presence-only occurrences with remote-sensing habitat covariates can fill knowledge gaps useful for informing conservation action. Applying this spatial information to conservation planning would ensure almost full protected area coverage for this endangered raptor. For tropical forest habitat specialists, we recommend that potential predictors derived from remote sensing, such as vegetation indices and biophysical measures, are considered as covariates, along with other variables including climate and topography.

Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction

• Interaction between vibratory roller and ground is simulated via 3D finite element method considering soil-structure interaction. • The comprehensive dataset is utilised to correlate the predicted ground modulus via machine learning algorithm extended Support Vector Regression. • The proposed extended Support Vector algorithm with Gaussian kernel and Generalised Gegenbauer Kernel functions could predict the double-layered soil stiffness. • It was observed that selection of 40% of data for training was the optimum in terms of satisfying both accuracy and minimized computational time. Intelligent Compaction (IC) has been acquiring a growing interest in real-time quality control of compacted soil layers because of its high efficiency and full-area coverage. The current intelligent compaction technology allows the determination of the uniformity level of compaction over large areas according to the dynamic response of the roller. However, accurate real-time determination of the soil modulus during compaction based on roller acceleration has been challenging due to the multi-layered composite nature of the soil and the nonlinearities of the governing dynamic equations of motion and soil response. This study adopts a double-layered soil profile, and a three-dimensional finite element model, accounting for soil-drum interaction, is utilised for the analysis. The isotropic hardening elastoplastic hysteretic model was implemented to simulate the soil behaviour subjected to cyclic loading ranging from small to large strain amplitudes and account for stiffness degradation. The comprehensive dataset composed of the roller acceleration response and ground characteristics is then used to correlate the predicted soil modulus via an advanced machine learning approach. The adopted machine learning method incorporating Gaussian Kernel and Generalised Gegenbauer Kernel functions can reasonably predict the double-layered soil modulus during roller compaction. Additional analyses were conducted to observe the proper training size and number of iterations to achieve real-time quality control to be used by site engineers. Furthermore, the influences of the relative modulus ratio, drum length and top layer modulus on the soil surface dynamic displacement are discussed.

Effect of Recombinant Human Lubricin on Model Tear Film Stability.

PurposeTo investigate and quantify the effect of recombinant human lubricin (rh-lubricin) on model tear film stability.MethodsA custom-built, interferometry-based instrument called the Interfacial Dewetting and Drainage Optical Platform was used to create and record the spatiotemporal evolution of model acellular tear films. Image segmentation and analysis was performed in MATLAB to extract the most essential features from the wet area fraction versus time curve, namely the evaporative break-up time and the final wet area fraction (A10). These two parameters indicate the tear film stability in the presence of rh-lubricin in its unstressed and stressed forms.ResultsOur parameters successfully captured the trend of increasing tear film stability with increasing rh-lubricin concentration, and captured differences in rh-lubricin efficacy after various industrially relevant stresses. Specifically, aggregation and fragmentation caused by a 4-week, high temperature stress condition negatively impacted rh-lubricin's ability to maintain model tear film stability. Adsorbed rh-lubricin alone was not sufficient to resist break-up and maintain full area coverage of the model tear film surface.ConclusionsOur results demonstrate that fragmentation and aggregation can negatively impact rh-lubricin's ability to maintain a stable tear film. In addition, the ability of rh-lubricin to maintain wetted area coverage is due to both freely dispersed and adsorbed rh-lubricin.Translational RelevanceOur platform and analysis method provide a facile, intuitive, and clinically relevant means to quantify the effect of ophthalmic drugs and formulations intended for improving tear film stability, as well as capture differences between variants related to drug stability and efficacy.

UAV-based persistent full area coverage with dynamic priorities

This paper tackles the problem of performing persistent and complete coverage of a target space with a minimum number of Unmanned Aerial Vehicles (UAVs). The UAVs are required to preferentially visit subareas of interest with higher frequency than the rest of the surveillance area within a predefined time threshold. A Minimum Spanning Tree based recursive algorithm is firstly proposed to estimate the upper bound of minimum number of UAVs for area coverage with visiting frequency constraints. Then an autonomous path planning strategy is proposed for persistent coverage, where a combinatorial priority function is designed as the goal assignment strategy and a modified Dijkstra algorithm is applied as the goal planning to obtain the optimum path. Finally, computer simulations have been conducted for the evaluation of the proposed solution, and the obtained results show that these algorithms can compute valid and sound solutions under different setups for the UAVs. • Propose a Minimum Spanning Tree based algorithm to get the minimum UAVs for coverage. • Construct a cost map with idle time and distances for goal assignment. • Propose an autonomous algorithm for persistent area coverage with dynamic priorities.

An Energy-Efficient Aerial Backhaul System With Reconfigurable Intelligent Surface

In this paper, we propose a novel wireless architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). By installing RIS on the aerial platform, rich line-of-sight and full-area coverage can be achieved, thereby, overcoming the limitations of the conventional terrestrial RIS. We consider a scenario where a sudden increase in traffic in an urban area triggers authorities to rapidly deploy unmanned-aerial vehicle base stations (UAV-BSs) to serve the ground users. In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS so that it successfully reaches the UAV-BSs. We jointly optimize the placement and array-partition strategies of aerial-RIS and the phases of RIS elements, which leads to an increase in energy-efficiency of every UAV-BS. We show that the complexity of our algorithm can be bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.

Emergent Search of UAV Swarm Guided by the Target Probability Map

In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms.

Binarization Mechanism Evaluation for Water Ingress Detectability in Honeycomb Sandwich Structure Using Lock-In Thermography.

The growing use of composite honeycomb structures in several industries including aircraft has demonstrated the need to develop effective and efficient non-destructive evaluation methods. In recent years, active thermography has attracted great interest as a reliable technology for non-destructive testing and evaluation of composite materials due to its advantages of non-contact, non-destructive, full-area coverage, high speed, qualitative, and quantitative testing. However, non-uniform heating, low spatial resolution, and ambient environmental noise make the detection and characterization of defects challenging. Therefore, in this study, lock-in thermography (LIT) was used to detect water ingress into an aircraft composite honeycomb sandwich structure, and the phase signals were binarized through the Otsu algorithm. A square composite honeycomb with dimensions of 210 mm × 210 mm along with 16 different defective areas of various sizes in groups filled with water by 25%, 50%, 75%, and 100% of the cell volume was considered. The sample was excited at multiple modulation frequencies (i.e., 1 Hz to 0.01 Hz). The results were compared in terms of phase contrast and CNR according to the modulation frequency. In addition, the detectability was analyzed by comparing the number of pixels of water ingress in the binarized image and the theoretical calculation.

An Evidential Approach for Area Coverage in Mobile Wireless Sensor Networks

The study of coverage problem in uncertain WSN environment requires the consideration of this uncertainty by taking the best possible decisions, since it is impossible to explicitly represent all the combinatorics to produce a conditional active/passive state nodes' planning in the area of interest, and allow reasoning on various environmental states of the partially known physical world. This paper addresses the problem of area coverage based on the Dempster-Shafer theory. The authors aim to ensure the full area coverage while using a subset of connected nodes, with minimal costs using a minimal number of dominant nodes regardless of the type of used deployment (random or deterministic). This is ensured by activating a single node in each subset of each geographic sub-area, thus extending the lifetime of the wireless sensor network to its maximum. The comparison of the proposed model denoted evidential approach for area coverage (EAAC) with two well-known protocols and with a recent one showed a better performance and a slight improvement in the covered area.

Uncertainty and Sensitivity Analysis of a Remote-Sensing-Based Penman–Monteith Model to Meteorological and Land Surface Input Variables

This study analysed the uncertainty and sensitivity of core and intermediate input variables of a remote-sensing-data-based Penman–Monteith (PM-Mu) evapotranspiration (ET) model. We derived absolute and relative uncertainties of core measured meteorological and remote-sensing-based atmospheric and land surface input variables and parameters of the PM-Mu model. Uncertainties of important intermediate data components (i.e., net radiation and aerodynamic and surface resistances) were also assessed. To estimate the instrument measurement uncertainties of the in situ meteorological input variables, we used the reported accuracies of the manufacturers. Observational accuracies of the remote sensing input variables (land surface temperature (LST), land surface emissivity (εs), leaf area index (LAI), land surface albedo (α)) were derived from peer-reviewed satellite sensor validation reports to compute their uncertainties. The input uncertainties were propagated to the final model’s evapotranspiration estimation uncertainty. Our analysis indicated relatively high uncertainties associated with relative humidity (RH), and hence all the intermediate variables associated with RH, like vapour pressure deficit (VPD) and the surface and aerodynamic resistances. This is in contrast to other studies, which reported LAI uncertainty as the most influential. The semi-arid conditions and seasonality of the regional South African climate and high temporal frequency of the variations in VPD, air and land surface temperatures could explain the uncertainties observed in this study. The results also showed the ET algorithm to be most sensitive to the air-land surface temperature difference. An accurate assessment of those in situ and remotely sensed variables is required to achieve reliable evapotranspiration model estimates in these generally dry regions and climates. A significant advantage of the remote-sensing-based ET method remains its full area coverage in contrast to classic-point (station)-based ET estimates.

Throughput-aware path planning for UAVs in D2D 5G networks

Abstract Unmanned Ariel Vehicles (UAVs) face increasing challenges in obtaining sensory data and transferring them to the user even before the completion of their flight for time-critical processing. Traditionally bounded by only area coverage and battery capacity, UAVs now need to meet network QoS requirement when streaming data. The emergence of 5G Device-to-Device (D2D) Networks enables high speed network communication for UAVs to transfer data via D2D links during a flight. The planning of UAV flight paths is now subject to both battery capacity and network quality of service (QoS) constraints. In this paper, we focus on the path planning for UAVs, which stream data to a data receiver machine, under the constraints of full area coverage and network throughput. We present a mathematical model to formulate the issue as a combinatorial optimization problem that attempts to minimize the flight cost of multiple UAVs covering the entire area. We show that the problem is NP-hard, therefore propose a heuristic method to derive the number of UAVs and determine their flight paths. The solution is proved to be bound by K ⋅ O P T . We conduct simulations to evaluate how the size of the area and the maximal flight distance of a UAV affect the number of UAVs needed, and how the D2D channel parameters affect the link throughputs.

The cooling efficiency of variable greenery coverage ratios in different urban densities: A study in a subtropical climate

Abstract Urban greenery, especially trees, has been proven to be one of the effective measures for urban heat island mitigation. However, no consistent findings have been found for the relationship between greenery abundance and cooling magnitude; while some previous studies discovered a linear relationship, others opined it could a non-linear one. In addition, there are rare studies exploring whether or not the strength of the relationship is dependent on urban density. Therefore, in this study, we aim to discuss cooling efficiency by measuring the relationship between greenery coverage ratio and the cooling effects of greenery. Parametric studies were conducted in a validated ENVI-met model, with different combinations of urban densities (Low, Mid, and High) and tree coverage ratios (2–30% at 2% interval, and 56% for full area coverage other than building). Then the pattern of the cooling efficiency was explored under three urban densities scenarios and two selected temporal periods on a typical summer daytime. For a subtropical climate background, results showed a non-linear (logarithmic) pattern for tree coverage ratio (TCR) and cooling effects, irrespective of the urban density, temporal periods, and heat indicators. When TCR reached 20–30%, the optimal cooling efficiency of trees were achieved, irrespective of building densities and temporal periods. The optimal threshold for greenery coverage in this study can provide science-based suggestions to urban planners and designers for better microclimate and thermal comfort environments at the neighborhood scale.

Thermographic inspection of water ingress in composite honeycomb sandwich structure: a quantitative comparison among Lock-in thermography algorithms

ABSTRACT The increasing use of honeycomb structures in the aircraft industry has demonstrated the need for the development of more effective evaluation methodologies. In recent years, thermography has received vast and growing attention as a reliable technique for non-destructive testing and evaluation of composite materials due to its remarkable advantages of non-contact, full area coverage, high speed, safe and convenient operation, qualitative and quantitative inspection capabilities. In this paper, Lock-in thermography is employed to inspect water ingress in aircraft honeycomb structures, and an investigation into the effect of water content on the quantitative estimation has been carried out. A square-shaped composite honeycomb panel of dimension 300 × 300 mm with four equal size defective areas of 50 × 50 mm as the groups of cells filled with water by 25%, 50%, 75% and 100% of the cell volume was considered as a test sample. The sample was excited at multiple modulation frequencies, viz., 0.5 Hz, 0.2 Hz, 0.1 Hz, 0.05 Hz, 0.02 Hz, and 0.01 Hz. Three Lock-in thermography algorithms, namely, Fast Fourier transform, Harmonic approximation, and Principle component analysis, are discussed for quantitative assessment; and their performance is compared in terms of Signal to noise ratio.

Large-Scale Synthesis of MoS2 Monolayer Using Na-Containing Facilitators for Electromechanical Energy Harvesting

The reliable synthesis method of large-area, continuous 2D MoS2 layer has been demanded for applications of next generation electronic and optoelectronic devices. Here, Na2S is introduced as a novel growth promoter to control over the grain size of full area coverage MoS2 monolayer. With Na2S solution-coated film, the nucleation density increases by the formation of reduced phase like NaMoO3-x. At the same time, additional sulfur from Na2S film affects the nucleation and growth mechanism resulting in changes of the domain geometry from triangular nanosheets to centimeter scale films. Moreover, by modulating the concentration of Na2S solution, the grain size of MoS2 monolayer has been changed. Thus, we propose a new approach of modulating nucleation density and grain morphology in polycrystalline full-coverage MoS2 films by using growth promoter, Na2S. The successful large-scale monolayer has been characterized for electromechanical energy conversion. As a result, a peak output of 253mV and 22nA was obtained when a strain of 0.26% was applied. Our results suggest the potential of large-scale 2D layer as an electromechanical energy converter.

Properties of Nitrogen/Silicon Doped Vertically Oriented Graphene Produced by ICP CVD Roll-to-Roll Technology

Simultaneous mass production of high quality vertically oriented graphene nanostructures and doping them by using an inductively coupled plasma chemical vapor deposition (ICP CVD) is a technological problem because little is understood about their growth mechanism over enlarged surfaces. We introduce a new method that combines the ICP CVD with roll-to-roll technology to enable the in-situ preparation of vertically oriented graphene by using propane as a precursor gas and nitrogen or silicon as dopants. This new technology enables preparation of vertically oriented graphene with distinct morphology and composition on a moving copper foil substrate at a lower cost. The technological parameters such as deposition time (1–30 min), gas partial pressure, composition of the gas mixture (propane, argon, nitrogen or silane), heating treatment (1–60 min) and temperature (350–500 °C) were varied to reveal the nanostructure growth, the evolution of its morphology and heteroatom’s intercalation by nitrogen or silicon. Unique nanostructures were examined by FE-SEM microscopy, Raman spectroscopy and energy dispersive X-Ray scattering techniques. The undoped and nitrogen- or silicon-doped nanostructures can be prepared with the full area coverage of the copper substrate on industrially manufactured surface defects. Longer deposition time (30 min, 450 °C) causes carbon amorphization and an increased fraction of sp3-hybridized carbon, leading to enlargement of vertically oriented carbonaceous nanostructures and growth of pillars.