Normal Map Research Articles

Diesel Engine Acoustic Emission Airflow Clogging Diagnostics With Machine Learning

A diesel engine electrical generator set (“gen-set”) was instrumented with in-cylinder indicating sensors as well as acoustic emission microphones near the engine. Air filter clogging was emulated by progressive restriction of the engine's inlet air flow path during which comprehensive engine and acoustic data were collected. Fast Fourier transforms (FFTs) were analyzed on the acoustic data. Dominant FFT peaks were then applied to supervised machine learning neural network analysis with matlab-based tools. The progressive detection of the air path clogging was audibly determined with correlation coefficients greater than 95% on test data sets for various FFT minimum intensity thresholds. Further, unsupervised machine learning self-organizing maps (SOMs) were produced during normal-baseline operation of the engine. The degrading air flow engine sound data were then applied to the normal-baseline operation SOM. The quantization error (QE) of the degraded engine data showed clear statistical differentiation from the normal operation data map. This unsupervised SOM-based approach does not know the engine degradation behavior in advance, yet shows clear promise as a method to monitor and detect changing engine operation. Companion in-cylinder combustion data additionally shows the degrading nature of the engine's combustion with progressive airflow restriction (richer and lower density combustion).

Chaotic attractor of the normal form map for grazing bifurcations of impact oscillators

Abstract Grazing bifurcations can cause impact oscillators to exhibit chaotic motions. Such dynamical behaviour can be described by a normal form map (called the Nordmark map). A main feature of the Nordmark map is that it has a square-root term. The purpose of this paper is to study the structure of the chaotic attractor of the Nordmark map from the topological point of view. First, the trapping region of the asymptotic dynamics of the map is constructed. It is then proven that, for some set of parameter values having positive Lebesgue measure, the ω -limit set of each point of the trapping region is contained in a invariant set which is just the closure of the unstable manifold of the hyperbolic fixed point of the map. Besides, the dynamics on the invariant set is topologically mixing. Accordingly the invariant set is a chaotic attractor of the map.

Reflectance and Shape Estimation with a Light Field Camera Under Natural Illumination

Reflectance and shape are two important components in visually perceiving the real world. Inferring the reflectance and shape of an object through cameras is a fundamental research topic in the field of computer vision. While three-dimensional shape recovery is pervasive with varieties of approaches and practical applications, reflectance recovery has only emerged recently. Reflectance recovery is a challenging task that is usually conducted in controlled environments, such as a laboratory environment with a special apparatus. However, it is desirable that the reflectance be recovered in the field with a handy camera so that reflectance can be jointly recovered with the shape. To that end, we present a solution that simultaneously recovers the reflectance and shape (i.e., dense depth and normal maps) of an object under natural illumination with commercially available handy cameras. We employ a light field camera to capture one light field image of the object, and a 360-degree camera to capture the illumination. The proposed method provides positive results in both simulation and real-world experiments.

RECREATING CULTURAL HERITAGE ENVIRONMENTS FOR VR USING PHOTOGRAMMETRY

Abstract. In this paper, we propose a workflow for recreating places of cultural heritage in Virtual Reality (VR) using structure from motion (SfM) photogrammetry. The unique texture of heritage places makes them ideal for full photogrammetric capture. An optimized model is created from the photogrammetric data so that it is small enough to render in a real-time environment. The optimized model, combined with mesh maps (texture maps, normal maps, etc.) looks like the original high detail model. The capture of a whole space makes it possible to create a VR experience with six degrees of freedom (6DoF) that allows the user to explore the historic place. Creating these experiences can bring people to cultural heritage that is either endangered or too remote for some people to access. The workflow described in this paper will be demonstrated with the case study of Myin-pya-gu, an 11th century temple in Bagan, Myanmar.

Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition.

A novel weighted hybrid classifier and a high-order, local normal derivative pattern descriptor is proposed for 3D face recognition. The Local derivative pattern (LDP) captures detailed information, based on the local derivative variation in different directions. The LDP is computed on three normal maps in x, y, and z directions and on different scales. The surface normal captures the orientation of a surface at each point of 3D data. More informative local shape information is extracted using the surface normal, as compared to depth. The nth-order LDP on the surface normal is proposed to encode more detailed features from the (n-1)th-order's local derivative direction variations. An extreme learning machine (ELM) based autoencoder, using a multilayer network structure, is employed to select more discriminant features and provide a faster training speed. A weighted hybrid framework is proposed to handle facial challenges using a combination of the ELM and the sparse representation classifier (SRC). The advantage of speed for the ELM and accuracy for the SRC in a weighted scheme is used to enhance the performance of the recognition system. Experimental results regarding four famous 3D face databases illustrate the generalization and effectiveness of the proposed method in terms of both computational cost and recognition accuracy.

Dexmedetomidine and fentanyl combination versus dexmedetomidine and pethidine as sedatives during colonoscopy

Background and aim Endoscopy procedures are generally performed under sedation, which ranging from minimal sedation to deep sedation. Conscious sedation during colonoscopic procedures provides a high level of patient and physician satisfaction. An ideal sedative agent should allow rapid modification of the sedation level by modifying the dose and should not have any adverse effects. Because such an ideal sedative agent does not exist, the combination use of dexmedetomidine with opioids (fentanyl or pethidine) may have the advantages over the use of a single agent. The goal of this study was to assess the effect of dexmedetomidine in combination with fentanyl or pethidine as a sedative for outpatient colonoscopy on hemodynamics, level and onset of sedation, analgesia, and degree of satisfaction of the colonoscopist and patients. Patients and methods A total of 60 colonoscopy patients between 21 and 60 years of age were included in the study. The patients scheduled for elective outpatient colonoscopy (with conscious sedation) were randomized into two groups: group 1 ‘DF group’ (N=30): dexmedetomidine 1 μ/kg/h infusion by syringe pump along with 1 μ/kg fentanyl was administered before the start of colonoscopy as a single shot, and Group 2 ‘DP group’ (N=30): dexmedetomidine 1 μ/kg/h infusion by syringe pump along with 1 mg/kg pethidine was administered before the start of colonoscopy as a single shot. The assessment includes heart rate (HR, beats/min), mean arterial blood pressure (MAP, mmHg), oxygen saturation at baseline and every 5 min, onset and level of sedation by Ramsay Sedation Score (RSS) after administration of the drugs, Numeric Pain Rating Scale at the end of colonoscopy, and the degree of satisfaction of the patients and the colonoscopist. Results The average value of the 6 h measurements in fentanyl group was 75.6±0.91 beats/min; whereas in pethidine group was 92.2±0.67 beats/min, with highly significant statistical difference (P<0.001), whereas there were nonsignificant differences regarding MAP and oxygen saturation. There was a highly significant decrease in the onset of sedation and increase in RSS and Numeric Pain Rating Scale in fentanyl group compared with pethidine group (P<0.01 for all). Moreover, there were nonsignificant differences regarding the degree of satisfaction for the colonoscopist and the patient (P>0.05). Multiple regression analysis shows that the increase in baseline HR had an independent effect on increasing onset of sedation (P<0.0001). Moreover, the fentanyl usage and the decrease in baseline HR had an independent effect on increasing RSS (P<0.01). Conclusion Patients who underwent colonoscopy and received dexmedetomidine-fentanyl regimen showed better hemodynamics (decreased HR, normal MAP, and oxygen saturation measurements), along with rapid onset of sedation and satisfied RSS compared with patients who received dexmedetomidine–pethidine regimen. In contrast, pethidine group showed better analgesia than fentanyl group. Both groups showed comparable satisfaction results in the colonoscopist and patients.

Performance Evaluation of Multi-UAV System in Post-Disaster Application: Validated by HITL Simulator

This paper proposes an evaluation of unmanned aerial vehicles (UAVs) performance in the mapping of disaster-struck areas. Sendai city in Japan, which was struck by the Tohoku earthquake/tsunami disaster in 2011, was mapped using multi-heterogeneous UAV. Normal mapping and searching missions are challenging as human resources are limited, and rescue teams are always needed to participate in disaster response mission. Mapping data and UAV performance evaluation will help rescuers to access and commence rescue operations in disaster-affected areas more effectively. Herein, flight plan designs are based on the information recorded after the disaster and on the mapping capabilities of the UAVs. The numerical and statistical results of the mapping missions were validated by executing the missions on real-time flight experiments in a simulator and analyzing the flight logs of the UAVs. After considering many factors and elements that affect the outcomes of the mapping mission, the authors provide a significant amount of useful data relevant to real UAV modules in the market. All flight plans were verified both manually and in a hardware-in-the-loop simulator developed by the authors. Most of the existing simulators support only a single UAV feature and have limited functionalities such as the ability to run different models on multiple UAVs. The simulator demonstrated the mapping and fine-tuned flight plans on an imported map of the disaster. As revealed in the experiments, the presented results and performance evaluations can effectively distribute different UAV models in post-disaster mapping missions.

Second-Order Variational Analysis of Parametric Constraint and Variational Systems

This paper is devoted to the generalized differential study of the normal cone mappings associated with a large class of parametric constraint systems (PCS) that appear, in particular, in nonpolyhedral conic programming. Conducting a local second-order analysis of such systems, we focus on computing the (primal-dual) graphical derivative of the normal cone mapping under the $C^2$-cone reducibility of the constraint set together with the fairly weak metric subregularity constraint qualification and its uniform parametric counterpart known as Robinson stability. The obtained precise formulas for computing the underlying second-order object are applied to the derivation of comprehensive conditions ensuring the important stability property of isolated calmness for solution maps to parametric variational systems associated with the given PCS.

L-cube polynomial for the recognition of normal and hypertensive string-like pulse mappings in Chinese medicine

L-cube polynomial for the recognition of normal and hypertensive string-like pulse mappings in Chinese medicine

Metric subregularity and/or calmness of the normal cone mapping to the p-order conic constraint system

For a finite convex function, we show that its subdifferential mapping is metrically subregular if and only if the normal cone mapping to its epigraph is metrically subregular. Then, for the nonconvex composition $$\psi =\theta \circ G$$ where G is a continuously differentiable mapping and $$\theta $$ is an extended real-valued function, we develop a criterion to identify the metric subregularity and calmness of the subdifferential mapping of $$\psi $$ in terms of that of the subdifferential mapping of $$\theta $$ . Together with the existing results, we obtain the metric subregularity of the normal cone mapping to the vector and matrix p-order cone $$K_p$$ and the conic constraint system $$g^{-1}(K_p)$$ with $$p\in [1,2]\cup \{+\infty \}$$ , where g is a continuously differentiable mapping. We also establish the calmness of the normal cone mapping to $$K_p$$ and $$g^{-1}(K_p)$$ with $$p\in [2,+\infty ]\cup \{1\}$$ .

Practical dynamic facial appearance modeling and acquisition

We present a method to acquire dynamic properties of facial skin appearance, including dynamic diffuse albedo encoding blood flow, dynamic specular intensity, and per-frame high resolution normal maps for a facial performance sequence. The method reconstructs these maps from a purely passive multi-camera setup, without the need for polarization or requiring temporally multiplexed illumination. Hence, it is very well suited for integration with existing passive systems for facial performance capture. To solve this seemingly underconstrained problem, we demonstrate that albedo dynamics during a facial performance can be modeled as a combination of: (1) a static, high-resolution base albedo map, modeling full skin pigmentation; and (2) a dynamic, one-dimensional component in the CIE L*a*b* color space, which explains changes in hemoglobin concentration due to blood flow. We leverage this albedo subspace and additional constraints on appearance and surface geometry to also estimate specular reflection parameters and resolve high-resolution normal maps with unprecedented detail in a passive capture system. These constraints are built into an inverse rendering framework that minimizes the difference of the rendered face to the captured images, incorporating constraints from multiple views for every texel on the face. The presented method is the first system capable of capturing high-quality dynamic appearance maps at full resolution and video framerates, providing a major step forward in the area of facial appearance acquisition.

Robust flow-guided neural prediction for sketch-based freeform surface modeling

Sketching provides an intuitive user interface for communicating free form shapes. While human observers can easily envision the shapes they intend to communicate, replicating this process algorithmically requires resolving numerous ambiguities. Existing sketch-based modeling methods resolve these ambiguities by either relying on expensive user annotations or by restricting the modeled shapes to specific narrow categories. We present an approach for modeling generic freeform 3D surfaces from sparse, expressive 2D sketches that overcomes both limitations by incorporating convolution neural networks (CNN) into the sketch processing workflow. Given a 2D sketch of a 3D surface, we use CNNs to infer the depth and normal maps representing the surface. To combat ambiguity we introduce an intermediate CNN layer that models the dense curvature direction, or flow, field of the surface, and produce an additional output confidence map along with depth and normal. The flow field guides our subsequent surface reconstruction for improved regularity; the confidence map trained unsupervised measures ambiguity and provides a robust estimator for data fitting. To reduce ambiguities in input sketches users can refine their input by providing optional depth values at sparse points and curvature hints for strokes. Our CNN is trained on a large dataset generated by rendering sketches of various 3D shapes using non-photo-realistic line rendering (NPR) method that mimics human sketching of free-form shapes. We use the CNN model to process both single- and multi-view sketches. Using our multi-view framework users progressively complete the shape by sketching in different views, generating complete closed shapes. For each new view, the modeling is assisted by partial sketches and depth cues provided by surfaces generated in earlier views. The partial surfaces are fused into a complete shape using predicted confidence levels as weights. We validate our approach, compare it with previous methods and alternative structures, and evaluate its performance with various modeling tasks. The results demonstrate our method is a new approach for efficiently modeling freeform shapes with succinct but expressive 2D sketches.

Analytical-numerical studies on the stability and bifurcations of periodic motion in the vibro-impact systems with clearances

This paper investigates the stability and bifurcations of periodic solutions in three-degree-of-freedom vibro-impact systems based on the explicit critical criteria and the discontinuous mapping method. Firstly, a six-dimensional Poincaré map is established by taking the impact surface as the Poincaré section. The explicit criteria including eigenvalue assignment and transversality condition are applied to determine the bifurcation point of co-dimension one pitchfork bifurcation. The stability and direction of the bifurcation solution are then studied by using center manifold reduction theory and normal form approach. Secondly, the bifurcation points of co-dimension-two Hopf–Hopf interaction bifurcation and pitchfork–Neimark–Sacker bifurcation are determined by applying the explicit critical criteria, and the local dynamic behaviors are examined in the neighborhood of these co-dimension-two bifurcation points. Finally, a six-dimensional Poincaré map formed by choosing the constant phase angle as the Poincaré section is used to investigate the existence and stability of grazing bifurcation based on the piecewise compound normal form map. The causes of the discontinuous jump and the coexistence of attractors near the grazing periodic motion are explained for the three-degree-of-freedom vibro-impact system with a moving constraint.

Ordered group-valued probability, positive operators, and integral representations

Probability maps are additive and normalised maps taking values in the unit interval of a lattice ordered Abelian group. They appear in theory of affine representations and they are also a semantic counterpart of Hajek's probability logic. In this paper we obtain a correspondence between probability maps and positive operators between certain Riesz spaces, which extends the well-known representation theorem of real-valued MV-algebraic states by positive linear functionals. When the codomain algebra contains all continuous functions, the set of all probability maps is convex, and we prove that its extreme points coincide with homomorphisms. We also show that probability maps can be viewed as a collection of states indexed by maximal ideals of a codomain algebra and we characterise this collection in special cases.

Polarized Light Field Imaging for Single-Shot Reflectance Separation.

We present a novel computational photography technique for single-shot separation of diffuse/specular reflectance, as well as novel angular domain separation of layered reflectance. We present two imaging solutions for this purpose: two-way polarized light-field (TPLF) imaging and four-way polarized light-field (FPLF) imaging. TPLF imaging consists of a polarized light-field camera, which simultaneously captures two orthogonal states of polarization. A single photograph of a subject acquired with the TPLF camera under polarized illumination then enables standard separation of diffuse (depolarizing) and polarization preserving specular reflectance using light-field sampling. We further demonstrate that the acquired data also enable novel angular separation of layered reflectance including separation of specular reflectance and single scattering in the polarization preserving component, as well as separation of shallow scattering from deep scattering in the depolarizing component. FPLF imaging further generalized the functionality of TPLF imaging under uncontrolled unpolarized or partially polarized illumination such as outdoors. We apply our approach for efficient acquisition of facial reflectance including diffuse and specular normal maps and novel separation of photometric normals into layered reflectance normals for layered facial renderings. We validate our proposed single-shot layered reflectance separation under various imaging conditions and demonstrate it to be comparable to an existing multi-shot technique that relies on structured lighting while achieving separation results under a variety of illumination conditions.

Second-order variational analysis in second-order cone programming

The paper conducts a second-order variational analysis for an important class of nonpolyhedral conic programs generated by the so-called second-order/Lorentz/ice-cream cone \({\mathcal {Q}}\). From one hand, we prove that the indicator function of \({\mathcal {Q}}\) is always twice epi-differentiable and apply this result to characterizing the uniqueness of Lagrange multipliers together with an error bound estimate in the general second-order cone programming setting involving twice differentiable data. On the other hand, we precisely calculate the graphical derivative of the normal cone mapping to \({\mathcal {Q}}\) under the metric subregularity constraint qualification and then give an application of the latter result to a complete characterization of isolated calmness for perturbed variational systems associated with second-order cone programs. The obtained results seem to be the first in the literature in these directions for nonpolyhedral problems without imposing any nondegeneracy assumptions.

A microfacet-based BRDF for the accurate and efficient rendering of high-definition specular normal maps

Complex specular microstructures found in glittery, scratched or brushed metal materials exhibit high-frequency variations in reflected light intensity. These variations are important for the human eye and give materials their uniqueness and personality. To model such microsurfaces, high-definition normal maps are very effective. The works of Yan et al. (ACM Trans Graph 33(4):116:1–116:9, 2014; ACM Trans Graph 35(4):56:1–56:9, 2016) enable the rendering of such material representations by evaluating a microfacet-based BRDF related to a whole ray footprint. Still, in specific configurations and especially at grazing angles, their method does not fully capture the expected material appearance. We propose to build upon their work and tackle the problem of accuracy using a more physically based reflection model. To do so, the normal map is approximated with a mixture of anisotropic, noncentered Beckmann normal distribution functions from which a closed form for the masking–shadowing term can be derived. Based on our formal definition, we provide a fast approximation leading to a performance overhead varying from 5 to 20% compared to the method of Yan et al. (2016). Our results show that we more closely match ground truth renderings than their methods.

Unexplained Progressive Visual Field Loss in the Presence of Normal Retinotopic Maps.

Lesions of primary visual cortex or its primary inputs typically result in retinotopically localized scotomas. Here we present an individual with unexplained visual field loss and deficits in visual perception in the absence of structural damage to the early visual pathway or lesions in visual cortex. The subject, monocular from an early age, underwent repeated perimetry tests over 8 years demonstrating severe anopia of the lower hemifield, and a clockwise progression of the loss through her upper left visual field. Her visual impairment was evident in a number of standardized tests and psychophysics, especially in tasks assessing spatial integration using illusory contours. However, her intellectual ability was intact and her performance in some other tasks assessing color vision or object detection in scenes was normal. We employed functional magnetic resonance imaging (fMRI), electroretinography and visually evoked potentials. Surprisingly, in contrast to the participant’s severe anopia, we found no evidence of abnormal function of her early visual pathways. Specifically, we performed retinotopic mapping using population receptive field (pRF) analysis to map the functional organization of visual cortex in the anopic participant and three control participants on two occasions three and a half years apart. Despite the behavioral visual field loss, her retinotopic maps and pRF parameters in visual areas V1–V3 were qualitatively normal. Further behavioral experiments confirmed that this discrepancy was not trivially explained by the difference between stimuli used for retinotopic mapping and perimetry. Structural T1 scans were normal at both time points, and volumetric analysis of white and gray matter tissue on the segmented T1 volumes did not reveal any abnormalities or deterioration over time. Our findings suggest that normal functional organization of early visual cortex without evident structural damage to the early visual pathway as disclosed by the techniques employed in this study does not necessarily guarantee conscious perception across the visual field.

A cost of musical training? Sensorimotor flexibility in musical sequence learning.

We report an experiment that tested the flexibility of sensorimotor learning in sequence production. Nonpianists and pianists learned simple melodies by ear under one of two auditory feedback conditions: one with normal pitch mapping (higher pitches to the right) and one with an inverted (reversed) mapping. After learning, both groups played melodies from memory while experiencing each feedback condition. Both groups exhibited sensorimotor learning and produced fewer errors at test while hearing the feedback used during training as opposed to the alternate feedback condition. However, learning was unstable for pianists who learned melodies with an inverted feedback condition, who produced more errors at test than pianists who learned melodies with normal-pitch mapping. Acquiring musical skill may therefore constrain subsequent sensorimotor flexibility.

Layouts From Panoramic Images With Geometry and Deep Learning

In this letter, we propose a novel procedure for three-dimensional layout recovery of indoor scenes from single 360 $^\circ$ panoramic images. With such images, all scene is seen at once, allowing us to recover closed geometries. Our method combines strategically the accuracy provided by geometric reasoning (lines and vanishing points) with the higher level of data abstraction and pattern recognition achieved by deep learning techniques (edge and normal maps). Thus, we extract structural corners from which we generate layout hypotheses of the room assuming Manhattan world. The best layout model is selected, achieving good performance on both simple rooms (box-type), and complex shaped rooms (with more than four walls). Experiments of the proposed approach are conducted within two public datasets, SUN360 and Stanford (2D-3D-S) demonstrating the advantages of estimating layouts by combining geometry and deep learning and the effectiveness of our proposal with respect to the state of the art.