Scalability Metrics Research Articles

Rapid and optimized parallel attribute reduction based on neighborhood rough sets and MapReduce

Attribute reduction is a crucial step in data pre-processing and feature engineering. It is the selection of a subset of relevant data attributes to reduce the computational complexity of machine learning models and improve their performance. Neighborhood rough set (NRS) theory provides a valuable framework for attribute reduction. It leverages neighborhood information to identify non-redundant and informative attributes for data analysis and machine learning tasks. Attribute subsets based on NRS theory are highly qualitative, producing effective prediction accuracies in Euclidean space. However, existing NRS-based solutions are resource-intensive because of the large search space required for finding neighborhoods and redundant computations. To overcome these limitations, we propose the rapid and optimized attribute reduction (ROAR) algorithm that optimizes the current state-of-the-art attribute-reduction method in NRS theory. The strength of ROAR lies in its ability to accelerate computations by rapidly determining the neighborhood consistency of data samples and consequently expediting the identification of both positive and boundary regions. This efficiency significantly enhances the overall processing time for the data analysis tasks. Experimental results on 12 standard datasets demonstrate that the ROAR algorithm exhibits high efficiency by obtaining accurate reduction results with rapid response times. To ensure that the ROAR algorithm is suitable for high-dimensional datasets, we provide a parallel implementation, namely, the P-ROAR algorithm. The P-ROAR algorithm is the first parallel attribute-reduction algorithm in the classical NRS theory. Computational speeds and scalability metrics establish that P-ROAR is much faster and more scalable for datasets with an enormous attribute space. These algorithms provide a tool for handling feature reduction in data engineering without compromising accuracy and performance.

Objective performance indicators during specific steps of robotic right colectomy can differentiate surgeon expertise

BackgroundCurrent surgical assessment tools are subjective and nonscalable. Objective performance indicators, calculated from robotic systems data, provide automated data regarding surgeon movements and robotic arm kinematics. We identified objective performance indicators that significantly differed among expert and trainee surgeons during specific steps of robotic right colectomy. MethodsEndoscopic videos were annotated to delineate surgical steps during robotic right colectomies. Objective performance indicators were compared during mesenteric dissection, ascending colon mobilization, hepatic flexure mobilization, and bowel preparation for transection. ResultsTwenty-five robotic right colectomy procedures (461 total surgical steps) performed by 2 experts and 8 trainees were analyzed. Experts exhibited faster camera acceleration and jerk during all steps, as well as faster dominant and nondominant arm acceleration and dominant arm jerk during all steps except distal bowel preparation. During mesenteric dissection, experts used faster camera and dominant arm velocity. During medial-to-lateral ascending colon mobilization, experts used less-dominant wrist yaw and pitch, faster nondominant arm velocity, shorter dominant arm path length, and shorter moving times for camera, dominant arm, and nondominant arm. During lateral-to-medial ascending colon mobilization, experts had faster dominant and nondominant arm velocity and third-arm acceleration. During hepatic flexure mobilization, experts exhibited more camera movements, greater velocity for camera, dominant and nondominant arms, and faster third-arm acceleration. During distal bowel preparation, experts used greater dominant wrist articulation, faster camera velocity, and longer nondominant arm path length. During proximal bowel preparation, experts demonstrated faster nondominant arm velocity. ConclusionObjective performance indicators can differentiate experts from trainees during distinct steps of robotic right colectomy. These automated, objective and scalable metrics can provide personalized feedback for trainees.

Analysis of Real-time Processing Approaches for Large Data Volumes in Metering Infrastructure

Smart grid systems and communication technologies, such as Advanced Metering Infrastructure (AMI), have revolutionized utility service management and monitoring. AMI leverages smart meters equipped with advanced communication capabilities, facilitating bidirectional communication between utilities and consumers. The increasing deployment of smart meters and the adoption of sub-hourly data collection requirements by utility companies highlight significant data volume growth. Thus, there is a need for efficient real-time data processing solutions as existing approaches may not meet previously established Service-Level Agreements (SLAs) concerning performance, accuracy, and scalability metrics. This research aims to comprehensively review the latest publications relevant to distributed real-time data processing methods for smart grid applications and outline problems for further research. Specifically, the study delves into the effectiveness and application of reviewed approaches in managing the constant stream of data from smart meters and IoT devices within the smart grid context. By analysing existing methodologies and advancements, this study seeks to identify challenges and opportunities in real-time data processing for smart grid infrastructures, focusing on addressing the complexities of processing, managing, and storing large volumes of real-time data. The literature review revealed two primary applications of real-time data processing: optimization of data streaming performance and data analysis. The review encompasses various studies, each presenting distinct methodologies and technologies applied to address the challenges of processing large volumes of real- time data from smart meters and IoT devices. Future research should address the challenges and limitations discovered in this study.

Neutralized, intense-ion beams for fusion

A charge- and current-neutralized, intense-ion beam (IIB) will propagate undeflected in a magnetized plasma by the E×B , or diamagnetic, drift for a distance large compared to the beam-ion gyro-radius. This propagation occurs because of collective plasma effects when the beam-energy density is sufficient to sustain the polarization-electric field, or diamagnetic-screening currents. Our principal interest is the E×B drift, with β=Ebeam/Efield<1 . IIBs are characterized by parameters in the range: 0.1–2 MeV ion energy, 1–100 MA m−2 ion-current density, and 17–350 kW average power produced on repetitively-pulsed systems. Multi-MW average-beam power is possible with appropriate modifications to the power supply and ion-source/accelerator. IIBs can be focused geometrically, and/or magnetically, to spot sizes of the order of a few cm’s and their angular divergence is typically, ∼15 mRadians, suitable for long-range propagation in a vacuum beam-line. IIBs lose energy and momentum in the same manner as particles injected by a neutral-beam injector (NBIs), hence could be useful for fusion applications, including: heating, current drive, fueling, profile modifications, etc and such applications have yet to be thoroughly studied. Summarized here are the physical principles accounting for IIB propagation; ion-source designs used to produce the IIB; and pulsed-power methods for energizing the IIB accelerator. This technology base informs scalable metrics for the ion-source/accelerator (excluding the HV power supply and interconnects) used in a conceptual injector that provides the same ion energy and injected power (1 MeV, 17 MW) as NBIs used on ITER. A comparison indicates that the IIB injector would be much smaller in size, lower cost, and have much greater efficiency, while also providing for real-time modulation of the beam energy and intensity and the use of small-diameter injection ports that could minimize fuel contamination and magnetic-field leakage between the IIB injector and tokamak.

Empirical Evaluation of Microgrid Fault Identification Models from a Statistical Perspective

Microgrid fault identification models are developed via integration of extensive data collection, pre-processing of collected data, current &amp; voltage segmentation, feature representation, identification of variant feature sets, their classification &amp; post-processing operations. Existing models that perform microgrid fault identification are either highly complex, or cannot be applied for heterogeneous fault types. Moreover, these models also showcase large variance in terms of their qualitative &amp; quantitative performance levels. Due to these issues, it is difficult for researchers to identify optimum models for their performance-specific deployment use cases. To overcome these issues, a detailed review of different microgrid fault detection &amp; mitigation models in needed, which can evaluate their performance in terms of qualitative &amp; quantitative parameters. Thus, this text initially discusses characteristics of some of the recently proposed microgrid fault detection models in terms of their functional nuances, application specific advantages, deployment specific limitations, and context-specific future research scopes. After referring this discussion, it was observed that linear models that incorporate pattern recognition are highly useful for fault pre-emption and mitigation purposes. This text also compares these models in terms of their accuracy of detection, delay needed for fault identification, computational complexity, deployment cost, and scalability metrics.

Spectral asynchrony as a measure of ecosystem response diversity

AbstractSpecies diversity is crucial for promoting ecosystem resilience and stability. Species diversity promotes complementarity in resource use, resulting in a wider range of responses to adverse conditions. This enables populations of different species to fluctuate asynchronously, maintaining ecosystem functioning during extreme climatic events. However, incorporating such mechanisms into conservation decisions and ecosystem modelling requires scalable metrics that represent species diversity, which is currently lacking. To address this, we introduce spectral asynchrony, a metric that captures the spatial heterogeneity of species’ functional responses occurring in distinct pixels. Here, we use remote sensing datasets to investigate the relationship between spectral asynchrony and productivity responses of seasonally dry tropical forests (SDTF) to climatic fluctuations. Our findings reveal that spectral asynchrony is associated with increased resistance and recovery of SDTF productivity in following extreme drought years, as well as greater productivity stability over two decades. Furthermore, higher spectral asynchrony was associated with relatively wetter regions, suggesting that increasing aridity across SDTF could potentially reduce landscape heterogeneity and limit ecosystem resilience to increasing droughts in the future. Spectral asynchrony provides an easily measurable and monitorable metric for assessing ecosystem responses to global changes, reflecting and scaling‐up the effects of species diversity at the local level.

A Mathematical Framework for Modeling Energy Dense Redox-Mediated Flow Batteries

Electrochemical processes can accelerate global decarbonization efforts by enabling the broad adoption of renewable electricity, facilitating the deployment of electric vehicles, and affording new opportunities in sustainable chemical manufacturing. However, this transition requires new electrochemical technologies with performance, cost, and scalability metrics that align with emerging demands. This need has inspired exploration into innovative systems that leverage unconventional materials and/or reactor designs. One such approach is the use of redox-mediated electrode reactions, which may expand the portfolio of feasible reactants, device configurations, and operating modes. In brief, a soluble and stable redox-couple (the mediator) is paired with an off-electrode material of interest (typically in the solid phase); this combination results in a two-step reaction system, where the mediator reacts on the electrode surface within an electrochemical cell before driving a separate redox reaction with the spatially removed material.1-2 Prior literature has shown how this approach can enhance the charge-storage capacity of redox flow batteries, facilitate difficult-to-perform electrochemical transformations, and unlock spatial and temporal flexibility in electrochemical processes.3-6 While promising, the performance of these systems is governed by coupled reactive-transport processes which present complex (and transient) interdependencies that frustrate navigation through the multi-dimensional design space. As such, technology development is slowed by the material- and time-intensive nature of experimental campaigns. To this end, modeling and simulation can offer an inexpensive and accelerated path to disaggregate interrelated physical processes, to quantify performance descriptors, and to inform experimental studies.7-8 In this presentation, we will describe a mathematical modeling framework that uses traditional chemical engineering principles to represent a redox-mediated electrochemical system consisting of a flow cell, a tank containing solid reactants, and a recirculating stream containing a soluble mediator. This domain aligns with common redox-mediated flow battery configurations, which enables both validation of the simulated electrochemical and fluid dynamic performance as well as insight into experimental observations reported in the literature. We will highlight the utility of low-dimensional models derived from this framework to provide deeper quantitative understanding into these phenomena. Through dimensional analyses, relevant dimensionless groups can be established that allow for generalization of model findings (e.g., scaling relationships, property tradeoffs) and articulation of design criteria for conceptual systems relevant to electrochemical energy storage and conversion. Acknowledgements This work was funded by the Skoltech – MIT Next Generation Program. N.J.M and B.J.N gratefully acknowledge the NSF Graduate Research Fellowship Program under Grant Number 2141064. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. CTM gratefully acknowledges support under and awarded by the Department of Defense, Office of Naval Research, through the National Defense Science and Engineering Graduate Fellowship.

A novel secure and distributed architecture for privacy-preserving healthcare system

Patients often visit several hospitals to obtain medication, generating a significant volume of data. Moreover, hospitals use different data analytic techniques to improve healthcare services, leading to patient data privacy. However, no integrated architecture has a trustworthy data repository and secure communication protocols to store and share the data with various parties (e.g., hospitals and data analysts) for different healthcare services. This study proposes an innovative three-tier secure and distributed privacy-preserving healthcare architecture that addresses the aforementioned challenges. The first tier introduces a trustworthy data repository called custodian, where the data owner stores encrypted data and offer real-time privacy-preserving health monitoring service. The second tier provides Elliptic Curve Cryptography-based authentication for secure data exchange between the data owner and the hospital. The third tier utilizes smart contracts and local differential privacy (LDP) for secure machine learning model training. All transactions are recorded on the blockchain and managed by a smart contract. Security analysis shows that the proposed framework ensures privacy, security, and data integrity. Performance analysis is done based on score metrics, scalability metrics, and formal analysis. A transaction takes 0.00121 s in the first tier, and in the second tier, it takes 0.1267 s. The third tier uses ɛ−LDP with a privacy budget of ɛ = 3 on Random Forest and achieves 97%, 83%, and 74% accuracy on breast cancer, heart disease, and diabetic datasets. This accuracy is higher than the previous state-of-the-art methods. Moreover, the proposed healthcare framework ensures privacy, security and outperforms the previous state-of-the-art methods.

Measuring real-time cognitive engagement in remote audiences

Responses to arts and entertainment media offer a valuable window into human behaviour. Many individuals worldwide spend the vast majority of their leisure time engaging with video content at home. However, there are few ways to study engagement and attention in this natural home viewing context. We used motion-tracking of the head via a web-camera to measure real-time cognitive engagement in 132 individuals while they watched 30 min of streamed theatre content at home. Head movement was negatively associated with engagement across a constellation of measures. Individuals who moved less reported feeling more engaged and immersed, evaluated the performance as more engaging, and were more likely to express interest in watching further. Our results demonstrate the value of in-home remote motion tracking as a low-cost, scalable metric of cognitive engagement, which can be used to collect audience behaviour data in a natural setting.

Scalable Incident Reporting Framework: A Sensor and IoT Research

The Internet of Things (IoT) is one of the most rapidly emerging technologies. It is observed that while many devices/machines get connected in an application, it is a challenge for the IoT application designer to keep the application scalable. Scalability is the ability of a device/application to adapt to the changes in the environment and meet the changing needs in the future. The paper presents a layered IoT architecture and discusses issues related to the scalability of each layer. The best open-source technologies are explored. A novel system architecture of a scalable IoT framework is conceptualized in this paper. An application covering vehicle accident reporting is designed with the proposed framework. The application is tested in real-time using the standalone hardware and its ability to report the incidents is confirmed. The scalability metrics of the proposed framework are evaluated and the results are reported.

SecIoTComm: An Actor-Based Model and Framework for Secure IoT Communication.

Internet of Things (IoT) ecosystems are becoming increasingly ubiquitous and heterogeneous, adding extra layers of complexity to secure communication and resource allocation. IoT computing resources are often located at the network edge and distributed across many heterogeneous sensors, actuators, and controller devices. This makes it challenging to provide the proper security mechanisms to IoT ecosystems in terms of manageability and maintainability. In an IoT ecosystem, computational resources are naturally distributed and shareable among their constituency, which creates an opportunity to distribute heavy tasks to them. However, resource allocation in IoT requires secure and complex communication and coordination mechanisms, which existing ones do not adequately support. In this paper, we present Secure Actor-based Model for IoT Communication (SecIoTComm), a model for representing secure IoT communication. SecIoTComm aims to represent secure IoT communication properties and design and implement novel mechanisms to improve their programmability and performance. SecIoTComm separates the communication and computation concerns, achieving design modularity in building IoT ecosystems. First, this paper presents the syntax and operational semantics of SecIoTComm. Then, we present an IoT framework implementing the key concepts of the model. Finally, we evaluate the developed framework using various performance and scalability metrics.

Simulation of 5G Mobile Core Network using SDN &amp; MPLS

In particular, in order to have the best possible understanding of the effects that program-able networking has, carry out a low cost analysis of both the (software define network) system and the (multiple protocol layer switching)&nbsp; system. This will give you the possible understanding of the effects that program-able networking has on the economics of a network in comparison to traditional networking, which is also known as MPLS technology. This is done so that a better understanding of the effects that program-able networking, also known as software-defined networking (SDN), technology has on the economics of a network can be gained. Conducting a quantitative investigation using an activity-based methodology to compute the (capital expenditure) and (operation expenditure) costs associated with a network was the first step in achieving this objective. This investigation was carried out.&nbsp; Second, in order to compare the aforementioned architectures in terms of their financial performances, we used the USC Scalability metric in addition to the Cost-to-Service metric. Both of these metrics were used. These models include (centralized control plane), (distributed control plane), and (hierarchical control plane). This was done in order to obtain the possible understanding of the different SDN plane systems that are currently in use. The separation of control plane and data plane that is achieved through the implementation of software-defined networking (SDN) makes it possible to program networks. Because of this separation, network operators and administrators are now in a position to make better use of the network's resources and have an easier time provisioning those resources when compared to the situation before the separation took place.

A configurable method for benchmarking scalability of cloud-native applications

Cloud-native applications constitute a recent trend for designing large-scale software systems. However, even though several cloud-native tools and patterns have emerged to support scalability, there is no commonly accepted method to empirically benchmark their scalability. In this study, we present a benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, frameworks, and deployment options. Our benchmarking method consists of scalability metrics, measurement methods, and an architecture for a scalability benchmarking tool, particularly suited for cloud-native applications. Following fundamental scalability definitions and established benchmarking best practices, we propose to quantify scalability by performing isolated experiments for different load and resource combinations, which asses whether specified service level objectives (SLOs) are achieved. To balance usability and reproducibility, our benchmarking method provides configuration options, controlling the trade-off between overall execution time and statistical grounding. We perform an extensive experimental evaluation of our method’s configuration options for the special case of event-driven microservices. For this purpose, we use benchmark implementations of the two stream processing frameworks Kafka Streams and Flink and run our experiments in two public clouds and one private cloud. We find that, independent of the cloud platform, it only takes a few repetitions (≤ 5) and short execution times (≤ 5 minutes) to assess whether SLOs are achieved. Combined with our findings from evaluating different search strategies, we conclude that our method allows to benchmark scalability in reasonable time.

Representations of Racial Minorities in Popular Movies

In the Hollywood film industry, racial minorities remain underrepresented. Characters from racially underrepresented groups receive less screen time, fewer central story positions, and frequently inherit plotlines, motivations, and actions that are primarily driven by White characters. Currently, there are no clearly defined, standardized, and scalable metrics for taking stock of racial minorities’ cinematographic representation. In this paper, we combine methodological tools from computer vision and network science to develop a content analytic framework for identifying visual and structural racial biases in film productions. We apply our approach on a set of 89 popular, full-length movies, demonstrating that this method provides a scalable examination of racial inclusion in film production and predicts movie performance. We integrate our method into larger theoretical discussions on audiences’ perception of racial minorities and illuminate future research trajectories towards the computational assessment of racial biases in audiovisual narratives.

Pragmatic evaluation of data mining models based on quality assessment & metric analysis

Data mining is the hearth of most modern day cyber physical deployments. Due to such large-scale use cases, selection of these models is of primary importance while developing cyber physical systems. But mining models are highly variant in terms of their internal performance characteristics, which makes it difficult for researchers to identify optimum models for their application-specific & performance-specific deployments. Moreover, these data mining models also vary in terms of their internal qualitative & quantitative performance measures. These measures include, precision, accuracy, recall, sensitivity, deployment cost, computational complexity, scalability, etc. Due to such a wide variation in performance, it is ambiguous for researchers to identify optimum models for their application deployments. To reduce this ambiguity, a comparison of these models in terms of their performance-level nuances, function advantages, deployment-specific limitations, and context-specific future scopes is described in this text. Researchers will be able to identify optimum models for their functional-specific deployments. It was observed that Neural Network (NN) based models including Convolutional NNs, Region based NNs, Recurrent NNs, etc. showcased better functional characteristics when compared with linear mining models for large-scale use cases. To further simplify model selection, this text compared the underlying models in terms of their performance metrics including accuracy, complexity, scalability, etc. Depending on this performance-specific evaluation, it was observed that bioinspired models when combined with deep learning techniques can outperform existing models for multiple application scenarios. This will further allow readers to identify optimum models based on their performance-specific characteristics. This text also evaluates a novel Mining Scalability Metric (MSM), which combines primary & secondary performance measures, and assists in identification of mining techniques that have higher accuracy, with lower complexity, and faster response, thereby reducing the ambiguity of model selection process.

Imagine a Walkable City: Physical activity and urban imageability across 19 major cities

Can the shape of a city promote physical activity? The question of why individuals engage in physical activity has been widely researched, but that research has predominantly focused on socio-demographic characteristics (e.g., age, gender, economic status) and coarse-grained spatial characteristics (e.g., population density), overlooking key urban characteristics of, say, whether a city is navigable or, as urban theorist Kevin Lynch put it, whether it is ‘imageable’ (whether its spatial configuration is economic of mental effort). That is mainly because, at scale, it is neither easy to model imageability nor feasible to measure physical activity. We modeled urban imageability with a single scalable metric of entropy, and then measured physical activity from 233K wearable devices over three years, and did so across 19 major cities in the developed world. We found that, after controlling for greenery, wealth, walkability, presence of landmarks, and weather conditions, the legibility hypothesis still holds: the more imageable a city, the more its dwellers engage in physical activity. Interestingly, wealth (GDP per capita) has a positive association with physical activity only in cities with inclement climate, effectively acting as a compensation mechanism for bad weather.

Improved High-Pressure, High-Temperature (HPHT) Materials Qualification Using Dissolved H2S Concentration as the Sour Service Scalable Metric

Gas phase H2S partial pressure (PH2S) is associated with sulfide stress cracking (SSC) and is routinely used as the “scalable” parameter to qualify materials for high-pressure, high-temperature (HPHT) wells. Candidate materials for HPHT wells routinely require ANSI/NACE MR0175/ISO 15156 compliance because a few mole ppm of H2S at high pressure may place the well beyond the 0.05 psia (0.3 kPa) sour service threshold. PH2S has been accepted historically as the scalable sour severity parameter. However, as the total pressure increases, the relationship between PH2S and the dissolved H2S concentration becomes nonlinear. This limits the robustness of PH2S as the sour severity metric. Thus, ISO 15156-1:2020 now permits the use of H2S fugacity (fH2S), H2S activity (aH2S), and H2S aqueous concentration (CH2S) as alternatives for sour testing. This recent revision is based on evidence that fH2S and CH2S each provide better correlations to SSC at elevated total pressures than PH2S. This paper will address the merits and challenges of using fH2S or CH2S to define sour severity: we argue that CH2S is a practical, experimentally verifiable approach, which can be used to validate ionic-equation of state frameworks used to characterize mildly sour HPHT environments.

Overview on the Blockchain-Based Supply Chain Systematics and Their Scalability Tools

Modern IT technologies shaped the shift in economic models with many advantages on cost, optimization, and time to market. This economic shift has increased the need for transparency and traceability in supply chain platforms to achieve trust among partners. Distributed ledger technology (DLT) is proposed to enable supply chains systems with trust requirements. In this paper, we investigate the existing DLT-based supply chain projects to show their technical part and limitations and extract the tools and techniques used to avoid the DLT scalability issue. We then set the requirements for a typical DLT-based supply chain in this context. The analyses are based on the scalability metrics such as computing, data storage, and transaction fees that fit the typical supply chain system. This paper highlights the effects of Blockchain techniques on scalability and their incorporation in supply chains systems. It also presents other existing solutions that can be applied to the supply chain. The investigation shows the necessity of having such tools in supply chains and developing them to achieve an efficient and scalable system. The paper calls for further scalability enhancements throughout introducing new tools and/or reutilize the current ones. Doi: 10.28991/esj-2021-SP1-04 Full Text: PDF

Performance-Based Analysis of Blockchain Scalability Metric

Cryptocurrencies like Bitcoin and Ethereum, are widely known applications of blockchain technology, have drawn much attention and are largely recognized in recent years. Initially Bitcoin and Ethereum processed 7 and 15 Transactions Per Second (TPS) respectively, whereas VISA and Paypal process 1700 and 193 TPS respectively. The biggest challenge to blockchain adoption is scalability, defined as the capacity to change the block size to handle the growing amount of load. This paper attempts to present the existing scalability solutions which are broadly classified into three layers: Layer 0 solutions focus on optimization of propagation protocol for transactions and blocks, Layer 1 solutions are based on the consensus algorithms and data structure, and Layer 2 solutions aims to decrease the load of the primary chain by implementing solutions outside the chain. We present a classification and comparison of existing blockchain scalability solutions based on performance along with their pros and cons

HIERARCHICAL PROXIMITY-BASED OVER-SEGMENTATION OF 3-D POINT CLOUDS FOR EFFICIENT GRAPH FEATURE DETECTION

Abstract. Point cloud simplification is empowered by the definition of similarity metrics which we aim to identify homogeneous regions within the point-cloud. Nonetheless, the variety of shapes and clutter in natural scenes, along with the significant resolution variations, occlusions, and noise, contribute to inconsistencies in the geometric properties, thereby making the homogeneity measurement challenging. Thus, the objective of this paper is to develop a point-cloud simplification model by means of data segmentation and to extract information in a better-suited way. The literature shows that most approaches either apply volumetric data strategies and/or resort to simplified planar geometries, which relate to only part of the entities found within a natural scene. To provide a more general strategy, we propose a proximity-based approach that allows an efficient and reliable surface characterization with no limitation on the number or shape of the primitives which in turn, enables detecting free-form objects. To achieve this, a local, computationally efficient and scalable metric is developed, which captures resolution variation and allows for short processing time. Our proposed scheme is demonstrated on datasets featuring a variety of surface types and characteristics. Experiments show high precision rates while exhibiting robustness to the varying resolution, texture, and occlusions that exist within the sets.