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Within the framework of the Bayesian approach, a method has been developed for recognizing signal-interference situations that arise during the operation of the information-measuring system of a moving object. The corresponding algorithms are presented. A method for correcting the weight of a priori information in the case of its significant difference from a posteriori information is proposed. One of the possible correction options using the so-called indicators of signals and interference accompanying signs is presented.

Estimating 6D poses of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the input image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using a disentangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.

Dynamic control of shape can bring multifunctionality to devices. Soft materials capable of programmable shape change require localized control of the magnitude and directionality of a mechanical response. We report the preparation of soft, ordered materials referred to as liquid crystal elastomers.The direction of molecular order, known as the director, is written within local volume elements (voxels) as small as 0.0005 cubic millimeters. Locally, the director controls the inherent mechanical response (55% strain) within the material. In monoliths with spatially patterned director, thermal or chemical stimuli transform flat sheets into three-dimensional objects through controlled bending and stretching. The programmable mechanical response of these materials could yield monolithic multifunctional devices or serve as reconfigurable substrates for flexible devices in aerospace, medicine, or consumer goods.

In this paper, the SIFT feature, HOG feature and SVM are adopted to design a movement vehicle recognition system, when the vehicle is relatively close to the camera and it backs to the camera, also the vehicle can be implemented to track. In this paper, two feature extraction methods, SIFT feature and HOG feature are respectively used to extract the features from 6000 images of vehicles with back facing the cameras and 6000 images without vehicles. Then SVM is used to train them to obtain a model. Then, we use the moving object recognition algorithm to extract the moving object in the given video, and put each moving object into the trained model to judge. If the model is judged as a vehicle, it will be tracked.

Additive manufacturing promises enormous geometrical freedom and the potential to combine materials for complex functions. The speed, geometry, and surface quality limitations of additive processes are linked to their reliance on material layering. We demonstrated concurrent printing of all points within a three-dimensional object by illuminating a rotating volume of photosensitive material with a dynamically evolving light pattern. We printed features as small as 0.3 millimeters in engineering acrylate polymers and printed soft structures with exceptionally smooth surfaces into a gelatin methacrylate hydrogel. Our process enables us to construct components that encase other preexisting solid objects, allowing for multimaterial fabrication. We developed models to describe speed and spatial resolution capabilities and demonstrated printing times of 30 to 120 seconds for diverse centimeter-scale objects.

In much recent thinking, social and cultural realms are thought of as existing prior to—or detached from—things, materiality, and landscape. It is often assumed, for example, that things are entirely 'constructed' by social or cultural perceptions and have no existence in and of themselves. Bjornar Olsen takes a different position. Drawing on a range of theories, especially phenomenology and actor-network-theory, Olsen claims that human life is fully mixed up with things and that humanity and human history emerge from such relationships. Things, moreover, possess unique qualities that are inherent in our cohabitation with them—qualities that help to facilitate existential security and memory of the past. This important work of archaeological theory challenges us to reconsider our ideas about the nature of things, past and present, demonstrating that objects themselves possess a dynamic presence that we must take into account if we are to understand the world we and they inhabit.

The bulk of Earth’s biological materials consist of few base substances—essentially proteins, polysaccharides, and minerals—that assemble into large varieties of structures. Multifunctionality arises naturally from this structural complexity: An example is the combination of rigidity and flexibility in protein-based teeth of the squid sucker ring. Other examples are time-delayed actuation in plant seed pods triggered by environmental signals, such as fire and water, and surface nanostructures that combine light manipulation with mechanical protection or water repellency. Bioinspired engineering transfers some of these structural principles into technically more relevant base materials to obtain new, often unexpected combinations of material properties. Less appreciated is the huge potential of using bioinspired structural complexity to avoid unnecessary chemical diversity, enabling easier recycling and, thus, a more sustainable materials economy.

Current X-ray imaging technologies involving flat-panel detectors have difficulty in imaging three-dimensional objects because fabrication of large-area, flexible, silicon-based photodetectors on highly curved surfaces remains a challenge 1 – 3 . Here we demonstrate ultralong-lived X-ray trapping for flat-panel-free, high-resolution, three-dimensional imaging using a series of solution-processable, lanthanide-doped nanoscintillators. Corroborated by quantum mechanical simulations of defect formation and electronic structures, our experimental characterizations reveal that slow hopping of trapped electrons due to radiation-triggered anionic migration in host lattices can induce more than 30 days of persistent radioluminescence. We further demonstrate X-ray luminescence extension imaging with resolution greater than 20 line pairs per millimetre and optical memory longer than 15 days. These findings provide insight into mechanisms underlying X-ray energy conversion through enduring electron trapping and offer a paradigm to motivate future research in wearable X-ray detectors for patient-centred radiography and mammography, imaging-guided therapeutics, high-energy physics and deep learning in radiology. Using lanthanide-doped nanomaterials and flexible substrates, an approach that enables flat-panel-free, high-resolution, three-dimensional imaging is demonstrated and termed X-ray luminescence extension imaging.

Additive manufacturing processes such as 3D printing use time-consuming, stepwise layer-by-layer approaches to object fabrication. We demonstrate the continuous generation of monolithic polymeric parts up to tens of centimeters in size with feature resolution below 100 micrometers. Continuous liquid interface production is achieved with an oxygen-permeable window below the ultraviolet image projection plane, which creates a \"dead zone\" (persistent liquid interface) where photopolymerization is inhibited between the window and the polymerizing part. We delineate critical control parameters and show that complex solid parts can be drawn out of the resin at rates of hundreds of millimeters per hour. These print speeds allow parts to be produced in minutes instead of hours.

Soft materials capable of transforming between three-dimensional (3D) shapes in response to stimuli such as light, heat, solvent, electric and magnetic fields have applications in diverse areas such as flexible electronics 1 , 2 , soft robotics 3 , 4 and biomedicine 5 – 7 . In particular, magnetic fields offer a safe and effective manipulation method for biomedical applications, which typically require remote actuation in enclosed and confined spaces 8 – 10 . With advances in magnetic field control 11 , magnetically responsive soft materials have also evolved from embedding discrete magnets 12 or incorporating magnetic particles 13 into soft compounds to generating nonuniform magnetization profiles in polymeric sheets 14 , 15 . Here we report 3D printing of programmed ferromagnetic domains in soft materials that enable fast transformations between complex 3D shapes via magnetic actuation. Our approach is based on direct ink writing 16 of an elastomer composite containing ferromagnetic microparticles. By applying a magnetic field to the dispensing nozzle while printing 17 , we reorient particles along the applied field to impart patterned magnetic polarity to printed filaments. This method allows us to program ferromagnetic domains in complex 3D-printed soft materials, enabling a set of previously inaccessible modes of transformation, such as remotely controlled auxetic behaviours of mechanical metamaterials with negative Poisson’s ratios. The actuation speed and power density of our printed soft materials with programmed ferromagnetic domains are orders of magnitude greater than existing 3D-printed active materials. We further demonstrate diverse functions derived from complex shape changes, including reconfigurable soft electronics, a mechanical metamaterial that can jump and a soft robot that crawls, rolls, catches fast-moving objects and transports a pharmaceutical dose. Programmed ferromagnetic domains are 3D-printed into soft materials capable of fast transformations between complex three-dimensional shapes via magnetic actuation.