Input Object Research Articles

Improved decryption quality with a random reference beam cryptosystem

The secure managing of sensible data is one of the main challenges nowadays. During the last years several advances have demonstrated the ability of optical encrypting systems to protect information. In spite of these developments, there are some evident issues to be solved. One of the main concerns is the limitation of the experimental cryptosystems in the size of the inputs that can be properly protected and recovered by the encrypting system. In the case of the experimental joint transform correlator (JTC) cryptosystem, the data to be encoded and the key are placed side by side in the input plane. Therefore, the size of the object to be encrypted is limited by the size of the encryption key and the separation between the input and the key. This limitation leads to degradation on the recovered data according the size and the spatial frequencies content of the object for a given recording media. In this work we experimentally implement an interferometric cryptosystem in which the encryption key is a ground glass diffuser (GGD) located in the reference arm. In the object arm, the information to be encoded is displayed in a spatial light modulator (SLM) and placed in contact with another GGD. This system allows the projection of the input object in the whole SLM area. The encryption process results from the interference between the reference beam and the Fourier transform of the input object plane. We experimentally analyze the quality of the decrypted object in relation with the size of the input original object. Then, we compare the performance of this system with the JTC cryptosystem. Finally, we implement a protocol for a multi-user environment under the rotation of the key. Experimental results show the viability, versatility and applicability of the proposal.

Investigation of a Capacitor Array of a Composite Capacitive Touch Panel

The work is devoted to studying a capacitor array of sensor and contactless identifier entry devices of the capacitive type on the example of sensory devices made from composite silver-containing materials on a ceramic substrate. The theoretical investigations involve calculations of the topology of the capacitor arrays, in particular, improved dimensions of the segments (3.5 × 3.5 mm) and the arrangement of array layers with the aim of more exactly recognizing input objects, reducing the malfunction of the controller, and increasing the electromagnetic pickup tolerance. In addition to searching for the dimensional parameters, the electrophysical parameters of the capacitive panel under study, namely, voltage, capacitance of 1.8 nF in the case of a matrix with square segments and 2.3 nF in the case of a matrix with round segments, and the charge before and after approaching the input objects (6 and 7 nC, respectively), are measured and calculated. Experimental investigations of the capacitor arrays are carried out, according to which it is determined that a round shape of the segments is preferable in order to increase the linearity of the dependence of the distance of the input objects from the centers of the sensor segments. It is also more effective to locate the integrated plate of a capacitor array from the bottom rather than the top. In this case, the set of holes in the array layers becomes unnecessary. The results of the calculations of the dimensional and electrophysical parameters and their experimental measurements help in the design of topologies of the arrays of the sensor elements of capacitive touch panels from materials which are an alternative to indium and tin oxides (ITOs) (silver nanowires, graphene, polymers, foil, silver-containing organic composites, etc.), as well as in the design of ADT diagrams and controllers for them.

KAJIAN KINERJA KENDALI PWM PADA RANCANG BANGUN SISTEM EFISIENSI AIR WUDHU MENGGUNAKAN SENSOR INFRA-MERAH

The use of water for wudhu activities in general uses a manually tapped faucet so that water from the tank will come out. But performing wudhu in this way can result in waste of water in the reservoir. If the faucet continues to burn during the ablution process, there will be a time when water is not needed, that is when the process of washing the body part intended in ablution, so that at that time the water will be wasted. Another problem is if the supply of water sources is limited or in difficult terrain. Therefore it is necessary to make a tool that can improve the effectiveness of using ablution water using infrared sensor application that will activate the valve and open the tap water automatically only if there is a hand or body part detected by the sensor. The speed of the water being pumped will be further slowed by using the PWM (Pulse Width Modulation) method according to the water level of the tank that is detected by the ultrasonic sensor. The results of the study have been done sensitivity of ultrasonic sensors to the PWM control linearity error of 2.02%. And Data Test results of faucet ablation water wudhu adjustable infrared sensor can detect the object distance as far as 5-30 cm. If the input object is more than 30 cm, then the water does not come out.

Singularity-constrained octahedral fields for hexahedral meshing

Despite high practical demand, algorithmic hexahedral meshing with guarantees on robustness and quality remains unsolved. A promising direction follows the idea of integer-grid maps, which pull back the Cartesian hexahedral grid formed by integer isoplanes from a parametric domain to a surface-conforming hexahedral mesh of the input object. Since directly optimizing for a high-quality integer-grid map is mathematically challenging, the construction is usually split into two steps: (1) generation of a surface-aligned octahedral field and (2) generation of an integer-grid map that best aligns to the octahedral field. The main robustness issue stems from the fact that smooth octahedral fields frequently exhibit singularity graphs that are not appropriate for hexahedral meshing and induce heavily degenerate integer-grid maps. The first contribution of this work is an enumeration of all local configurations that exist in hex meshes with bounded edge valence, and a generalization of the Hopf-Poincaré formula to octahedral fields, leading to necessary local and global conditions for the hex-meshability of an octahedral field in terms of its singularity graph. The second contribution is a novel algorithm to generate octahedral fields with prescribed hex-meshable singularity graphs, which requires the solution of a large nonlinear mixed-integer algebraic system. This algorithm is an important step toward robust automatic hexahedral meshing since it enables the generation of a hex-meshable octahedral field.

Metamolds

We propose a new method for fabricating digital objects through reusable silicone molds. Molds are generated by casting liquid silicone into custom 3D printed containers called metamolds. Metamolds automatically define the cuts that are needed to extract the cast object from the silicone mold. The shape of metamolds is designed through a novel segmentation technique, which takes into account both geometric and topological constraints involved in the process of mold casting. Our technique is simple, does not require changing the shape or topology of the input objects, and only requires of-the-shelf materials and technologies. We successfully tested our method on a set of challenging examples with complex shapes and rich geometric detail.

DSCarver

We present an automatic algorithm for subtractive manufacturing of freeform 3D objects using high-speed machining (HSM) via CNC. A CNC machine operates a cylindrical cutter to carve off material from a 3D shape stock, following a tool path, to "expose" the target object. Our method decomposes the input object's surface into a small number of patches each of which is fully accessible and machinable by the CNC machine, in continuous fashion, under a fixed cutter-object setup configuration. This is achieved by covering the input surface with a minimum number of accessible regions and then extracting a set of machinable patches from each accessible region. For each patch obtained, we compute a continuous, space-filling, and iso-scallop tool path which conforms to the patch boundary, enabling efficient carving with high-quality surface finishing. The tool path is generated in the form of connected Fermat spirals , which have been generalized from a 2D fill pattern for layered manufacturing to work for curved surfaces. Furthermore, we develop a novel method to control the spacing of Fermat spirals based on directional surface curvature and adapt the heat method to obtain iso-scallop carving. We demonstrate automatic generation of accessible and machinable surface decompositions and iso-scallop Fermat spiral carving paths for freeform 3D objects. Comparisons are made to tool paths generated by commercial software in terms of real machining time and surface quality.

Selection of Optimal Object Features in Object-Based Image Analysis Using Filter-Based Algorithms

With the increase in spatial resolution of recent sensors, object-based image analysis (OBIA) has gained importance for producing detailed land use maps. One of the main advantages of OBIA is that a variety of spectral, spatial and textural features can be extracted for the segmented image objects that are later utilized in classification. However, using a large number of features not only increases the required computational time, but also requires a large number of ground samples, which is unavailable in most cases. For these reasons, feature selection (FS) has become an important research topic for OBIA based classification studies. In this study, three filter-based FS algorithms namely, Chi square, information gain and ReliefF were applied to determine the most effective object features that ensure high separability among landscape features. For this purpose, importance degree (i.e. ranks) of 110 input object features were firstly estimated by the algorithms, and correlation-based merit function was then applied to determine optimum feature subset size. Multi-resolution segmentation algorithm was applied for segmenting a WorldView-2 image. Support vector machine, random forest and nearest neighbour classifiers were all utilized to classify segmented image objects using the selected object features. Results revealed that the FS algorithms were effective for selecting the most relevant features. Also, the classifiers produced the highest performances with 24 out of 110 features selected by the information gain (IG) algorithm. Particularly, the support vector machine classifier produced the highest overall accuracy (92.00%) with 24 selected features determined by the IG algorithm. A significant improvement of about 4% was achieved by applying FS procedures that was found statistically significant in terms of Wilcoxon signed-ranks test.

Packable Springs

AbstractLaser cutting is an appealing fabrication process due to the low cost of materials and extremely fast fabrication. However, the design space afforded by laser cutting is limited, since only flat panels can be cut. Previous methods for manufacturing from flat sheets usually roughly approximate 3D objects by polyhedrons or cross sections. Computational design methods for connecting, interlocking, or folding several laser cut panels have been introduced; to obtain a good approximation, these methods require numerous parts and long assembly times. In this paper, we propose a radically different approach: Our approximation is based on cutting thin, planar spirals out of flat panels. When such spirals are pulled apart, they take on the shape of a 3D spring whose contours are similar to the input object. We devise an optimization problem that aims to minimize the number of required parts, thus reducing costs and fabrication time, while at the same time ensuring that the resulting spring mimics the shape of the original object. In addition to rapid fabrication and assembly, our method enables compact packaging and storage as flat parts. We also demonstrate its use for creating armatures for sculptures and moulds for filling, with potential applications in architecture or construction.

Research on Distortion Invariant Recognition of a Planar Integrated Optical Correlator

In this paper, an optical correlator which adopts planar integrated 2F structure and optimal tradeoff synthetic discriminant function filter is proposed to improve the system integration and the recognition ability. The specialized digital microlens is designed for the correlator to replace the physical lens. By the theoretical analysis, the structural design conditions and parameters of the correlator are given, and the system volume is 63.1 cm3 which is only half of the planar integrated 4F optical correlator. Moreover, simulations and experimental research works reveal that the correlator can achieve stable recognition to the input object of distortion with scaling (52%, 150%), rotation (–50°, +42°), and existed scaling (72%, 130%) and rotation (–36°, +38°) simultaneously. Theoretical analysis and experimental results indicate that the planar integrated optical correlator with compact structure and high integration has good performance of distortion invariant recognition.

Near-convex decomposition and layering for efficient 3D printing

We introduce a novel divide-and-conquer approach for 3D printing, which provides automatic decomposition and configuration of an input object into print-ready components. Our method improves 3D printing by reducing material consumption, decreasing printing time, and improving fidelity of printed models. An input object is decomposed into a set of components obtained by a near-convex segmentation that minimizes an energy function. Then the configuration phase provides a robust algorithm to pack the components for an efficient print job. Our approach has been tested on both simulated models and real-world printed objects. Our results show that the framework can reduce print time by up to 65% (fused deposition modeling, or FDM) and 36% (stereolithography, or SLA) on average and diminish material consumption by up to 35% (FDM) and 10% (SLA) on consumer printers, while also providing more accurate objects.

Heat transfer analysis and resolution quantification of active dynamic thermography through human skin.

Active dynamic thermography (ADT) is a non-contact imaging technique that characterizes non-homogeneities in thermal conductance through objects as a response to applied energy stimulus. The aim of this study was to (i) develop a heat transfer model to define the relationship between thermal stimulation and resolution and (ii) empirically quantify the resolution an ADT imaging system can detect through a range of depths of human skin. A heat transfer model was developed to describe a thermally non-conductive object below a sheet of skin. The size and depth of the object were varied to simulate wound conditions, while the intensity and duration of thermal stimulation were varied to define stimulation parameters. The model was solved by numerical analysis. For ex vivo experimentation, freshly excised human pannus tissue was cut into sheets of thickness 2.54-6.35 × 10-4 m (0.010-0.025vinches) for a total of 48 grafts from 12 patients. Grafts were placed over a 3D printed resolution target with objects ranging from 0.445-0.125 LP/mm. Stimulation from a 300 W halogen lamp array was applied for 0.5-14 seconds for a total of 480 experiments. ADT resolved a peak of 0.428 ± 0.025 LP/mm for 2.54 × 10-4 m (0.010 inches) skin thickness, 0.384 ± 0.030 LP/mm for 3.81 × 10-4 m (0.015 inches), 0.325 ± 0.042 LP/mm for 5.08 × 10-4 m (0.020 inches) and 0.249 ± 0.057 LP/mm for 6.35 × 10-4 m (0.025 inches) skin thickness. Additionally, it was determined that the ideal duration of stimulation energy with a 300 W stimulation system was 4 seconds for 2.54 × 10-4 m, 6 seconds for 3.81 × 10-4 m, 8 seconds for 5.08 × 10-4 m, and 14 seconds for 6.35 × 10-4 m skin thickness. This study has characterized the correlation between thermal stimulus input and resolvable object size and depth for ADT. Through ex vivo experimentation it has also quantified the functional imaging depth to below the sub-cutis, beyond that of conventional imaging techniques. Lasers Surg. Med. © 2018 Wiley Periodicals, Inc.

Experimental study of frequency response in digital holography

In this paper, we present an experimental study of the frequency response in off-axis numerical reconstruction of digitally recorded holograms (DH). Despite of the fact that several works have reported the way how the digital holographic imaging systems deal with the frequency of the imaged objects, all of them are limited to computer modelling. Up to the best knowledge of the authors this is the first attempt to experimentally evaluate the response on frequency of DH. A star test of resolution is imaged in DH to measure the modulation transfer function (MTF), which is quantified as the ratio of the contrast of the reconstructed image over that of the input object. From the measured MTFs, trade-off guides in terms of the frequency response of the DH imaging system can be elaborated as the reduction of data to be processed or data-compressing is of interest.

Object based approach for electronic variable acoustics systems

Demand for spatially accurate high-quality sound reproduction in the audio entertainment industry is now at an entirely new level. In today’s world of immersive entertainment, sound reproduction is about more than just decibels—via the accurate localization of sound sources, performances can be immeasurably enhanced in classical concerts, operas, theatres, and every field of live entertainment. The days of two-dimensional PAs are over—a new era has arrived with 3D spatial sound and enhanced room acoustics. This paper describes a fully object-based approach to immersive sound reproduction, combining 3D positioning of audio sources and acoustic enhancement on the same unit by processing room reflections as audio objects. The algorithms combine the flexibility of traditional in-line digital reverberation devices with the quality and natural impression of a regenerative reverberation. The result of this process is a subset of discrete, early and late reverberant reflections which are convolved with the direct sound to serve as input objects for further processing. This unique object based system approach offers many advantages with respect to the traditional channel based approach. The technological principles of this approach, along with the results of successful applications throughout Europe, will be shown.

Object segmentation framework based on dictionary‐group and sparse shape representation

Given an input object whose shape is partly similar to some of the samples in the training set, a dictionary-group-based sparse model is introduced that can use the local information of those less similar shape neighbours to represent the object and guide the segmentation. The model follows from a new sparse energy function that combines a series of sparse local constraints with the fuzzy log-polar decomposition-based shape elements. Finally, a unified framework is built to connect the high-level shape representation with the low-level image segmentation. The model on the public datasets is tested, and the experimental results show the superior shape segmentation capabilities of the proposed model.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a “wake–sleep”-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for information abstraction and summarization. In this work, we present a generic model for personalized multilevel exploration and analysis over large dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale model. Considering different exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets.

Retargeting 3D Objects and Scenes with a General Framework

AbstractIn this paper, we introduce an interactive method suitable for retargeting both 3D objects and scenes. Initially, the input object or scene is decomposed into a collection of constituent components enclosed by corresponding control bounding volumes which capture the intra‐structures of the object or semantic grouping of objects in the 3D scene. The overall retargeting is accomplished through a constrained optimization by manipulating the control bounding volumes. Without inferring the intricate dependencies between the components, we define a minimal set of constraints that maintain the spatial arrangement and connectivity between the components to regularize the valid retargeting results. The default retargeting behavior can then be easily altered by additional semantic constraints imposed by users. This strategy makes the proposed method highly flexible to process a wide variety of 3D objects and scenes under an unified framework. In addition, the proposed method achieved more general structure‐preserving pattern synthesis in both object and scene levels. We demonstrate the effectiveness of our method by applying it to several complicated 3D objects and scenes.

Sparse RBF surface representations

In this paper, we propose a sparse surface representation for arbitrary surface models (point clouds, mesh models, continuous surface models, etc.). We approximate the input surface model with radial basis functions (RBF) whose centers are located on the medial axis of the input object surface. The sparsity of the RBF representation is achieved by solving an L1 optimization problem. Experimental results demonstrate that our method needs much less number of parameters to represent the input surface model with good accuracy. The sparse representation is useful in various applications, such as saving memory space in storing the surface models, and saving time in transmission of the surface models on the internet.

Sustainability Performance Measurement with Data Envelopment Analysis: A Combined Production- and Decision-Theoretic Approach

Decision-based production theory is a generalization of traditional production theories developed in order to integrate concerns of modern management science and economics, such as sustainability and environmental protection. The main idea is the distinction of technologically determined inputs and outputs of a production system’s activity from its desired or undesired impacts on (artificial or natural) environments, formalized by multiple value functions. The paper extends the literature on the generalization and theoretical foundation of data envelopment analysis (DEA) by decision-based production theory. It provides fruitful insights into some well-known pitfalls and protocols in DEA and is practically useful for the measurement of effectiveness in achieving different, often conflicting sustainability objectives. New results are concerned with hierarchies of DEA evaluations, undesired input and output objects, superfluous or inadequate disposability assumptions as well as problems of nonlinear value functions.