Basic Primitives Research Articles

Semi-Automated Segmentation of Geometric Shapes from Point Clouds

Building information models (BIM) in the civil industry are very popular nowadays. The basic information of these models is the 3D geometric model of a building structure. The most applied methodology to model the existing buildings is by generating 3D geometric information from point clouds provided by laser scanners. The fundamental principle of this methodology is the recognition of structures shaped in basic geometric primitives, e.g., planes, spheres, and cylinders. The basic premise of the efficiency of this methodology is the automation of detection, since manual segmentation of a point cloud can be challenging, time-consuming, and, therefore, inefficient. This paper presents a novel algorithm for the automated segmentation of geometric shapes in point clouds without needing pre-segmentation. With the designed algorithm, structures formed in three types of basic geometrical primitive can be identified and segmented: planar (e.g., walls, floors, ceilings), spherical (e.g., laser scanner reference targets), and cylindrical (e.g., columns, pillars, piping). The RANSAC paradigm partially inspires the proposed algorithm; however, various modifications must be made. The algorithm was tested on several point clouds and was compared with the standard RANSAC algorithm; this part is described in the last section of the paper. One of the tests was performed on a double cylinder-shaped test object, the parameters (radius and height) of this object were available with high accuracy (0.1 mm), and the differences between the known and estimated parameters were below 0.5 mm in each case, indicating the correctness of the proposed algorithm. Also, a comparison with the standard RANSAC algorithm was performed, where the algorithm proposed showed better results than the standard RANSAC algorithm. The segmentation quality was, on average, increased from 50% to 100%.

An impulse response formulation for small-sample learning and control of additive manufacturing quality

Machine learning for additive manufacturing (ML4AM) has emerged as a viable strategy in recent years to enhance 3D printing performance. However, the amount of data required for model training and the lack of ability to infer AM process insights can be serious barriers for black-box learning methods. Due to the nature of low-volume fabrication of infinite product variety in AM, ML4AM also faces “small data, big tasks” challenges to learn heterogeneous point cloud data and control the quality of new designs. To address these challenges, this work establishes an impulse response formulation of layer-wise AM processes to relate design inputs with the deformed final products. To enable prescriptive learning from a small sample of printed parts with different 3D shapes, we develop a fabrication-aware input–output representation, where each product is constructed by a large amount of basic shap primitives. The impulse response model depicts how the 2D shape primitives (circular sectors, line segments, and corner segments) in each layer are stacked up to become final 3D shape primitives. A geometric quality of a new design can therefore be predicted through the construction of learned shape primitives. Essentially, the small-sample learning of printed products is transformed into a large-sample learning of printed shape primitives under the impulse response formulation of AM. This fabrication-aware formulation builds the foundation for applying well-established control theory to the intelligent quality control in AM. It not only provides theoretical underpinning and justification of our previous work, but also enable new opportunities in ML4AM. As an example, it leads to transfer function characterization of AM processes to uncover process insights. It also provides block-diagram representation of AM processes to design and optimize the control of AM quality.

PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation

AbstractWe present PriFit, a semi‐supervised approach for label‐efficient learning of 3D point cloud segmentation networks. PriFit combines geometric primitive fitting with point‐based representation learning. Its key idea is to learn point representations whose clustering reveals shape regions that can be approximated well by basic geometric primitives, such as cuboids and ellipsoids. The learned point representations can then be re‐used in existing network architectures for 3D point cloud segmentation, and improves their performance in the few‐shot setting. According to our experiments on the widely used ShapeNet and PartNet benchmarks, PriFit outperforms several state‐of‐the‐art methods in this setting, suggesting that decomposability into primitives is a useful prior for learning representations predictive of semantic parts. We present a number of ablative experiments varying the choice of geometric primitives and downstream tasks to demonstrate the effectiveness of the method.

Primitive quantum gates for dihedral gauge theories

We describe the simulation of dihedral gauge theories on digital quantum computers. The nonabelian discrete gauge group $D_N$ -- the dihedral group -- serves as an approximation to $U(1)\times\mathbb{Z}_2$ lattice gauge theory. In order to carry out such a lattice simulation, we detail the construction of efficient quantum circuits to realize basic primitives including the nonabelian Fourier transform over $D_N$, the trace operation, and the group multiplication and inversion operations. For each case the required quantum resources scale linearly or as low-degree polynomials in $n=\log N$. We experimentally benchmark our gates on the Rigetti Aspen-9 quantum processor for the case of $D_4$. The fidelity of all $D_4$ gates was found to exceed $80\%$.

Development of Topology Optimized Bending-Twisting Soft Finger

Abstract This paper proposed a systematic framework to automatically design and fabricate optimized soft robotic fingers. The soft finger is composed of a soft silicone structure with inner air chambers and a harder outer layer, which are fabricated by molding process and 3D printing, respectively. The softer layer is utilized for actuation while the supportive hard structure is used to impose constraints. The framework applies a topology optimization approach based on rational approximation of material properties (RAMP) method to obtain an optimal design of the outer layer of the soft fingers. Two basic motion primitives (bending and twisting) of the soft finger were explored. A multi-segmented soft bending finger and a soft twisting finger were designed and fabricated through the proposed framework. This work also explored the combination of bending and twisting primitives by developing a combined bending-twisting soft finger. The soft fingers were characterized by free and blocked movement tests. The experiments showed that the triple-segmented soft finger can achieve a maximum of 50.5 deg no-load bending under the actuation pressure of 53 kPa. The blocked movement test on the multi-segmented soft actuating finger showed that this finger could generate up to a maximum of 0.63 N force under 57 kPa actuation pressure in 7 s of inflating time. The developed twisting soft finger was shown to achieve tip rotation of up to 219 deg under 29 kPa actuation pressure. Finally, the potential capability of the bending-twisting soft fingers was verified through applications like screwing and object grasping.

Modeling of SHF/EHF Radio-Wave Scattering for Curved Surfaces With Voxel Cone Tracing

Efficient and accurate radio propagation modeling is essential for optimization of both radio sensing and communication systems. However, highly accurate full-wave methods remain inefficient at high frequencies, as unit of computation has to be made much smaller than the wavelength. On the other hand, ray-based approaches offer the desired speed, but the surface element (typically, a triangle) must be made much larger than the wavelength, thus making it difficult to represent complex curved surfaces of common objects, such as cars or unmanned aerial vehicles. As a result, for super high frequency (SHF)/extremely high frequency (EHF) bands, it is challenging to select a method that is both fast and capable of capturing curved surfaces correctly. To address this issue, we present a method that offers a reasonable tradeoff between speed and accuracy for radio propagation modeling in the bands of interest. Specifically, we combine efficient voxel scene representation targeting a cone tracing algorithm with a statistical scattering model. To confirm the validity of our approach, we report the dependence of reflected power on the distance for basic primitives, such as cone and sphere, for which closed-form radar cross-section solutions are known. We demonstrate that the proposed approach is superior compared to the conventional ray-tracing solutions, while posing no restrictions for efficient voxel cone tracing implementations.

SPoTKD: A Protocol for Symmetric Key Distribution Over Public Channels Using Self-Powered Timekeeping Devices

In this paper, we propose a novel class of symmetric key distribution protocol that leverages basic security primitives offered by low-cost, hardware chipsets containing millions of synchronized self-powered timers. The keys are derived from the temporal dynamics of a physical, micro-scale time-keeping device which makes the keys immune to any potential side-channel attacks, malicious tampering, or snooping. Using the behavioral model of the self-powered timers, we first show that the derived key-strings can pass the randomness test as defined by the National Institute of Standards and Technology (NIST) suite. The key-strings are then used in two SPoTKD (Self-Powered Timer Key Distribution) protocols that exploit the timer’s dynamics as one-way functions: (a) protocol 1 facilitates secure communications between a user and a remote Server; and (b) protocol 2 facilitates secure communications between two users. In this paper, we investigate the security of these protocols under standard model and against different adversarial attacks. Using Monte-Carlo simulations, we also investigate the robustness of these protocols in the presence of real-world operating conditions and propose error-correcting SPoTKD protocols to mitigate these noise-related artifacts.

Automated Semantics and Topology Representation of Residential-Building Space Using Floor-Plan Raster Maps

Automatically representing the semantics and topology of indoor building spaces from floor-plans is necessary for many applications, such as architectural design and indoor renovations. Extensive studies have investigated reconstructing indoor spaces with semantics and topology using professional means (e.g., laser scanning and photogrammetry). Floor-plan raster maps are widely and freely available for various purposes. Nevertheless, there is little research on the semantic and topological representation of indoor elements from floor-plan raster maps. To fill this gap, we propose a method of automatically representing the semantics and topology of indoor spaces from floor-plan raster maps. The proposed method first identifies basic geometric primitives from floor-plans using a learning-based hierarchical segmentation approach. Second, the relationship between the detected geometric primitives is assembled into the planar structure representation with topological data using mixed integer programming. Finally, the floor-plan graph structure is checked and optimized to maintain consistency with a polygonal coordinate descent strategy, resulting in a correct representation of the semantics and topology of the indoor space. Comprehensive evaluations demonstrate that the proposed method effectively achieves superior performance in three different datasets. The proposed method allows for 3D model popups for better visualizations and direct architectural model manipulations of the interior building layouts computation.

Rethinking Embedded Blocks for Machine Learning Applications

The underlying goal of FPGA architecture research is to devise flexible substrates that implement a wide variety of circuits efficiently. Contemporary FPGA architectures have been optimized to support networking, signal processing, and image processing applications through high-precision digital signal processing (DSP) blocks. The recent emergence of machine learning has created a new set of demands characterized by: (1) higher computational density and (2) low precision arithmetic requirements. With the goal of exploring this new design space in a methodical manner, we first propose a problem formulation involving computing nested loops over multiply-accumulate (MAC) operations, which covers many basic linear algebra primitives and standard deep neural network (DNN) kernels. A quantitative methodology for deriving efficient coarse-grained compute block architectures from benchmarks is then proposed together with a family of new embedded blocks, called MLBlocks. An MLBlock instance includes several multiply-accumulate units connected via a flexible routing, where each configuration performs a few parallel dot-products in a systolic array fashion. This architecture is parameterized with support for different data movements, reuse, and precisions, utilizing a columnar arrangement that is compatible with existing FPGA architectures. On synthetic benchmarks, we demonstrate that for 8-bit arithmetic, MLBlocks offer 6× improved performance over the commercial Xilinx DSP48E2 architecture with smaller area and delay; and for time-multiplexed 16-bit arithmetic, achieves 2× higher performance per area with the same area and frequency. All source codes and data, along with documents to reproduce all the results in this article, are available at http://github.com/raminrasoulinezhad/MLBlocks .

Multiple Cylinder Extraction from Organized Point Clouds.

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.

Self-supporting structure design with feature-driven optimization approach for additive manufacturing

In this work, a topology optimization approach is developed for additive manufacturing (AM) of 2D and 3D self-supporting structures. Three important issues, i.e., overhang angle control, avoidance of the so-called V-shaped areas and minimum length scale control are addressed. 2D solid polygon and 3D polyhedron features are introduced as basic design primitives that are capable of translations, deformations and intersections to drive topological changes of the structure. The overhang angle control is realized in a straightforward way only by imposing upper bounds to related design variables without introducing any nonlinear constraint. The V-shaped area is avoided by simply limiting the positions of solid features. Minimum length scale control is controlled by a robust formulation. Numerical examples in 2D and 3D demonstrate the effectiveness of the proposed approach for various build directions, critical overhang angles and minimum length scales considered in AM.

Blockchain based secure smart city architecture using low resource IoTs

Internet of Things (IoT) is growing exponentially in research and industry. Although, many standard and conventional security solutions have been provided for IoT, it suffers from many privacy and security concerns. Standard security protocols are not suitable for majority of IoT devices because of its decentralized topology and resource-constraints. Blockchain (BC) finds its efficient application in IoT to preserve the five basic cryptographic primitives, such as confidentiality, authenticity, integrity, availability and non-repudiation. Adoption of conventional BC in IoT causes high energy consumption, delay and computational overheads which are not appropriate for various resource constrained IoT devices. To mitigate these problems, this work proposes a smart access control framework in a public and a private BC for a smart city application which makes it more efficient and secure as compared to the existing IoT applications. The proposed IoT based smart city architecture adopts BC technology for preserving all the cryptographic security and privacy issues. Moreover, proposed BC has minimal overhead as well. This work investigates the existing threat models and critical access control issues which handle multiple permissions of various processing nodes of IoT environment and detects relevant inconsistencies. Comparison in terms of all security issues with existing literature shows that the proposed architecture is competitively efficient in terms of security access control. The primary goal of this research article is to explore the possibility of BC as an alternative to standard security solutions for low resource IoT applications.

Knowledge-driven Natural Language Understanding of English Text and its Applications

Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by ``truly understanding'' the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy.

Implementing the Abstract MAC Layer via Inductive Coloring Under the Rayleigh-Fading Model

In this paper, we study distributed algorithms to realize efficient communications under the Rayleigh-fading model. This model extends the popular deterministic SINR model using stochastic propagations to address the fading effects observed in reality. Stochastic propagations can greatly increase the difficulty of handling interference and collisions, especially in a local context without much global knowledge. We present a new technique called Inductive Coloring that can be used to schedule fast transmissions with Rayleigh-fading interference. The computation of inductive coloring takes only O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n) time with the proposed distributed algorithm, where n is the number of nodes in the network. We illustrate the power of inductive coloring by giving a distributed and randomized algorithm to implement the abstract MAC (absMAC) layer, which was first proposed by Kuhn et al.. With two basic time-guaranteed communication primitives, namely acknowledgement and progress, which correspond to the operations of node local broadcasts and message receptions from others, the absMAC layer decomposes the algorithm design and analysis in networks into two independent components, i.e., implementing the absMAC layer over a physical network and designing algorithms with the help of the two primitives in the absMAC layer. Thus, it sharply reduces the fussy and complicated process of algorithm design and analysis over the physical network. Our proposed algorithm implements the absMAC layer under the Rayleigh-fading model with no more than a logarithmic factor inferior to the optimal solution in terms of time complexity. The presented simulation results indicate that our algorithm performs well in realistic environments. Furthermore, we show that by making full use of our proposed absMAC layer algorithm, many network primitives such as Neighbor Discovery, Single/Multiple-Message Broadcast, and Consensus, can be efficiently implemented.

Lexical Influences on Categorical Speech Perception Are Driven by a Temporoparietal Circuit.

Categorical judgments of otherwise identical phonemes are biased toward hearing words (i.e., "Ganong effect") suggesting lexical context influences perception of even basic speech primitives. Lexical biasing could manifest via late stage postperceptual mechanisms related to decision or, alternatively, top-down linguistic inference that acts on early perceptual coding. Here, we exploited the temporal sensitivity of EEG to resolve the spatiotemporal dynamics of these context-related influences on speech categorization. Listeners rapidly classified sounds from a /gɪ/-/kɪ/ gradient presented in opposing word-nonword contexts (GIFT-kift vs. giss-KISS), designed to bias perception toward lexical items. Phonetic perception shifted toward the direction of words, establishing a robust Ganong effect behaviorally. ERPs revealed a neural analog of lexical biasing emerging within ∼200 msec. Source analyses uncovered a distributed neural network supporting the Ganong including middle temporal gyrus, inferior parietal lobe, and middle frontal cortex. Yet, among Ganong-sensitive regions, only left middle temporal gyrus and inferior parietal lobe predicted behavioral susceptibility to lexical influence. Our findings confirm lexical status rapidly constrains sublexical categorical representations for speech within several hundred milliseconds but likely does so outside the purview of canonical auditory-sensory brain areas.

Reconciliation based key exchange schemes using lattices: a review

Lattice-based cryptography is one of the emerging fields of cryptography in the post-quantum world. It is resistive to quantum attacks and has performance competitive to that of prevalent cryptosystem such as Rivest–Shamir–Adleman (RSA), Diffie Hellman etc. Till now, various basic cryptographic primitives like encryption and decryption, digital signature, hash-based functions, and key exchange are proposed in lattice-based cryptography. The key exchange primitive is one of the basic cryptographic primitives of the Public Key Infrastructure (PKI). Lattices are preferably used to design provably secure reconciliation based key exchange protocols against quantum attacks. However, the literature pertaining to the study of reconciliation based key exchange protocols is limited and often the schemes are studied independently. Therefore, in this work, we have reviewed the reconciliation based key exchange schemes and classify these schemes under two different categories depending on the reconciliation mechanism used by the scheme. We also point out the basic key exchange schemes upon which all other key exchange schemes are based. We conduct a complete review, security analysis, implementation and comparison of these basic key exchange schemes.

Shape-Driven EIT Reconstruction Using Fourier Representations.

Shape-driven approaches have been proposed as an effective strategy for the electrical impedance tomography (EIT) reconstruction problem in recent years. In order to augment the shape-driven approaches, we propose a new method that transforms the shape to be reconstructed as basic primitives directly modeled by using Fourier representations. To allow automatic topological changes between the basic primitives and surrounding objects simultaneously, Boolean operations are employed. The Boolean operations with direct representation of primitives can be utilized for dimensionality and ill-posedness reduction, enabling feasible shape and topology optimization with shape-driven approaches. As a proof of principle, we leverage the proposed method for two dimensional shape reconstruction in EIT with various conductivity distributions. We demonstrate that our method is able to improve EIT reconstructions by enabling accurate shape and topology optimization.

Supershape Augmented Reconstruction Method Based on Boolean Operations in Electrical Impedance Tomography

This paper presents a supershape augmented shape reconstruction algorithm for Electrical impedance tomography, aiming at high-quality reconstructions with respect to multiphase conductivity distributions. Within the proposed algorithm, the reconstructed inclusions are composed of basic shape primitives expressed through a supershape formula. To allow automatic topological evolution, multiple Boolean operations, including three basic Boolean operations and compound Boolean operations, are employed so that the candidate shape primitives can be intersected, subtracted and merged to form different shapes. The proposed algorithm is tested using simulations and water tank experimental data. Moreover, robustness studies considering varying initial guesses and differing signal to noise ratios are performed. It is demonstrated that the proposed algorithm is able to preserve detailed features of inclusions under detection and provide reliable estimates to unknown conductivity values and the interface properties.

Modeling of stereometric figures in a browser using WebGL

The article is devoted to WebGL technology. Previously, web browsers could only display simple graphics using а processor. Today's adopted hardware acceleration support allows browsers to access the computer's GPU to render increasingly complex 3D graphics. To facilitate working with WebGL, you can use ready-made libraries, for example three.js. Thе article provides an overview of the three.js library tools for creating 3D shapes in the browser. Snippets of JavaScript code that implement basic 3D primitives are provided. Ways to apply textures are shown.The results presented in the article will be useful to teachers of informatics when used in additional classes or when organizing project and research activities of schoolchildren in informatics. Students will be able to familiarize themselves with the promising JavaScript language and gain basic 3D modeling skills.

SketchPatch

The paradigm of image-to-image translation is leveraged for the benefit of sketch stylization via transfer of geometric textural details. Lacking the necessary volumes of data for standard training of translation systems, we advocate for operation at the patch level, where a handful of stylized sketches provide ample mining potential for patches featuring basic geometric primitives. Operating at the patch level necessitates special consideration of full sketch translation, as individual translation of patches with no regard to neighbors is likely to produce visible seams and artifacts at patch borders. Aligned pairs of styled and plain primitives are combined to form input hybrids containing styled elements around the border and plain elements within, and given as input to a seamless translation (ST) generator, whose output patches are expected to reconstruct the fully styled patch. An adversarial addition promotes generalization and robustness to diverse geometries at inference time, forming a simple and effective system for arbitrary sketch stylization, as demonstrated upon a variety of styles and sketches.