Hidden Objects Research Articles

A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging.

In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions.

Review Paper on Analysis and Design of Residential G+4 Building by Using STAAD Pro

Abstract: It is important for designers to save time in order to be competitive in the increasingly competitive market. There are many ways to examine different frames using paper and pencil, such as the kani’s method, cantilever method, the portal method and the matrix method. However, with the help of the integration of various engineering methods, the determination of various changes in the calculation text (i.e. text-based), durability, deformation are failures caused by hidden objects has changed and brought this system to a new level.

Detecting Hidden and Irrelevant Objectives in Interactive Multiobjective Optimization

Evolutionary multi-objective optimization algorithms (EMOAs) typically assume that all objectives that are relevant to the decision-maker (DM) are optimized by the EMOA. In some scenarios, however, there are irrelevant objectives that are optimized by the EMOA but ignored by the DM, as well as, hidden objectives that the DM considers when judging the utility of solutions but are not optimized. This discrepancy between the EMOA and the DM’s preferences may impede the search for the most-preferred solution and waste resources evaluating irrelevant objectives. Research on objective reduction has focused so far on the structure of the problem and correlations between objectives and neglected the role of the DM. We formally define here the concepts of irrelevant and hidden objectives and propose methods for detecting them, based on uni-variate feature selection and recursive feature elimination, that use the preferences already elicited when a DM interacts with a ranking-based interactive EMOA (iEMOA). We incorporate the detection methods into an iEMOA capable of dynamically switching the objectives being optimized. Our experiments show that this approach can efficiently identify which objectives are relevant to the DM and reduce the number of objectives being optimized, while keeping and often improving the utility, according to the DM, of the best solution found.

Design and implementation of marine information management network security system based on artificial intelligence embedded technology

The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M.

Developmental trajectories of picture-based object representations during the first year of life.

Experience with an object's photograph changes 9-month-olds' preference for the referent object, confirming they can represent objects from pictures. However, picture-based representations appear weaker than object-based representations. The current study's first objective was to investigate age differences in object recognition memory after familiarization with objects' pictures. The second objective was to test whether age differences in object permanence sensitivity with picture-based representations match those found with object-based representations, whereby 7-month-olds search more for familiar hidden objects but 11-month-olds search more for novel ones. Six- and 11-month-olds were familiarized with an object's photo and tested on their representation of the real object by comparing their reaching for it versus a novel object. Objects were visible under conditions testing recognition memory and hidden under conditions testing object permanence. Like 9-month-olds, 6- and 11-month-olds preferred novelty with visible objects, showing early object recognition after picture familiarization, as well as developmental continuity. Unlike 9-month-olds, who switched to preferring familiarity with hidden objects, 6- and 11-month-olds switched to null preference. This pattern fails to match 7- and 11-month-olds' hidden-object preferences after familiarization with real objects, revealing discontinuity in sensitivity to object permanence after picture familiarization, and suggesting that picture-based representations are weaker than object-based ones.

Prior Knowledge Uses Prestimulus Alpha Band Oscillations and Persistent Poststimulus Neural Templates for Conscious Perception.

Prior knowledge has a profound impact on the way we perceive the world. However, it remains unclear how the prior knowledge is maintained in our brains and thereby influences the subsequent conscious perception. The Dalmatian dog illusion is a perfect tool to study prior knowledge, where the picture is initially perceived as noise. Once the prior knowledge was introduced, a Dalmatian dog could be consciously seen, and the picture immediately became meaningful. Using pictures with hidden objects as standard stimuli and similar pictures without hidden objects as deviant stimuli, we investigated the neural representation of prior knowledge and its impact on conscious perception in an oddball paradigm using electroencephalogram (EEG) in both male and female human subjects. We found that the neural patterns between the prestimulus alpha band oscillations and poststimulus EEG activity were significantly more similar for the standard stimuli than for the deviant stimuli after prior knowledge was provided. Furthermore, decoding analysis revealed that persistent neural templates were evoked after the introduction of prior knowledge, similar to that evoked in the early stages of visual processing. In conclusion, the current study suggests that prior knowledge uses alpha band oscillations in a multivariate manner in the prestimulus period and induces specific persistent neural templates in the poststimulus period, enabling the conscious perception of the hidden objects.SIGNIFICANCE STATEMENT The visual world we live in is not always optimal. In dark or noisy environments, prior knowledge can help us interpret imperfect sensory signals and enable us to consciously perceive hidden objects. However, we still know very little about how prior knowledge works at the neural level. Using the Dalmatian dog illusion and multivariate methods, we found that prior knowledge uses prestimulus alpha band oscillations to carry information about the hidden object and exerts a persistent influence in the poststimulus period by inducing specific neural templates. Our findings provide a window into the neural underpinnings of prior knowledge and offer new insights into the role of alpha band oscillations and neural templates associated with conscious perception.

Location-Free Camouflage Generation Network

Camouflage is a common visual phenomenon, which refers to hiding the foreground objects into the background images, making them briefly invisible to the human eye. Previous work has typically been implemented by an iterative optimization process. However, these methods struggle in 1) efficiently generating camouflage images using foreground and background with flexible structure; 2) camouflaging foreground objects to regions with multiple appearances (e.g. the junction of the vegetation and the mountains), which limit their practical application. To address these problems, this paper proposes a novel <italic/><b>L</b><i>ocation-free</i> <b>C</b><i>amouflage</i> <b>G</b><i>eneration</i> <b>Net</b><i>work</i> (<b>LCG-Net</b>) that fuse high-level features of foreground and background image, and generate result by one inference. Specifically, a <i>Position-aligned Structure Fusion</i> (PSF) module is devised to guide structure feature fusion based on the point-to-point structure similarity of foreground and background, and introduce local appearance features point-by-point. To retain the necessary identifiable features, a new immerse loss is adopted under our pipeline, while a background patch appearance loss is utilized to ensure that the hidden objects look continuous and natural at regions with multiple appearances. Experiments show that our method has results as satisfactory as state-of-the-art in the single-appearance regions and are less likely to be completely invisible, but far exceed the quality of the state-of-the-art in the multi-appearance regions. Moreover, our method is hundreds of times faster than previous methods. Benefitting from the unique advantages of our method, we provide some downstream applications for camouflage generation, which show its potential. The related code and dataset will be released at <uri>https://github.com/Tale17/LCG-Net</uri>.

Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

In a vehicular ad hoc network (VANET), a massive quantity of data needs to be transmitted on a large scale in shorter time durations. At the same time, vehicles exhibit high velocity, leading to more vehicle disconnections. Both of these characteristics result in unreliable data communication in VANET. A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability. Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs. But one such difficulty was reducing the cluster number under increasing transmitting nodes. This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering (EHOGO-DAC) Scheme for VANET. The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles. In addition, the DHOGO-EAC technique is mainly based on the HOGO algorithm, which is stimulated by old games, and the searching agent tries to identify hidden objects in a given space. The DHOGO-EAC technique derives a fitness function for the clustering process, including the total number of clusters and Euclidean distance. The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects. The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

Spectra-separated depth-of-field extended fluorescence imaging through scattering media using speckle deconvolution

Over the last decade, many methods have been proposed to achieve optical imaging through scattering media. Among them, speckle deconvolution utilizing the optical memory effect of the medium is widely employed due to its advantages in imaging speed and relatively simpler setup. However, practical applications of speckle deconvolution method are rarely demonstrated as most of the existing systems are based on transmission geometry to ensure direct incoherent illumination and easy detection. Here we propose an imaging system in reflection configuration that is able to retrieve the hidden fluorescent object behind a thin scattering media by speckle deconvolution. The object speckle pattern and speckle pattern from a point source (point spread function of the system, PSF) are spectrally separated to facilitate non-invasive image acquisitions. It is shown that, to reconstruct object faithfully, spectral de-correlation and chromatic aberration induced depth de-correlation between the object speckle pattern and the PSF have to be compensated. Speckle correlation is employed to characterize the depth-of-field (DOF) range in this reflection imaging system. Within the DOF, focus and defocus effects in the reconstructed objects are observed when rescaling the PSF onto different axial planes. Beyond the DOF, hidden objects that originally fail in reconstruction can be retrieved by PSF stacking and PSF rescaling. The proposed approach is potentially to find wide applications in biomedical imaging, ranging from deep tissue fluorescence microscopy to small animal imaging.

What aspects of counting help infants attend to numerosity?

Recent work shows that 18-month old infants understand that counting is numerically relevant-infants who see objects counted are more likely to represent the approximate number of objects in the array than infants who see the objects labeled but not counted. Which aspects of counting signal infants to attend to numerosity in this way? Here we asked whether infants rely on familiarity with the count words in their native language, or on procedures instantiated by the counting routine, independent of specific tokens. In three experiments (N=48), we found that 18-month old infants from English-speaking households successfully distinguished four hidden objects from two when the objects were counted correctly, regardless of their familiarity with the count words (i.e., when objects were counted in familiar English and in unfamiliar German). However, when the objects were counted using familiar English count words in ways that violated basic counting principles, infants no longer represented the arrays, failing to distinguish four hidden objects from two. Together with previous findings, these results suggest that children may link the procedure of counting with numerosity years before they learn the meanings of the count words.

Computational imaging of moving objects obscured by a random corridor via speckle correlations

Computational imaging makes it possible to reconstruct hidden objects through random media and around corners, which is of fundamental importance in various fields. Despite recent advances, computational imaging has not been studied in certain types of random scenarios, such as tortuous corridors filled with random media. We refer to this category of complex environment as a ’random corridor’, and propose a reduced spatial- and ensemble-speckle intensity correlation (RSESIC) method to image a moving object obscured by a random corridor. Experimental results show that the method can reconstruct the image of a centimeter-sized hidden object with a sub-millimeter resolution by a low-cost digital camera. The imaging capability depends on three system parameters and can be characterized by the correlation fidelity (CF). Furthermore, the RSESIC method is able to recover the image of objects even for a single pixel containing the contribution of about 102 speckle grains, which overcomes the theoretical limitation of traditional speckle imaging methods. Last but not least, when the power attenuation of speckle intensity leads to serious deterioration of CF, the image of hidden objects can still be reconstructed by the corrected intensity correlation.

High Gain Compact UWB Antenna for Ground Penetrating Radar Detection and Soil Inspection.

An ultrawide bandwidth (UWB) antenna for ground-penetrating radar (GPR) applications is designed to check soil moisture and provide good-quality images of metallic targets hidden in the soil. GPR is a promising technology for detecting and identifying buried objects, such as landmines, and investigating soil in terms of moisture content and contamination. A paddle-shaped microstrip antenna is created by cutting a rectangular patch at one of its diametrical edges fed by the coplanar waveguide technique. The antenna is loaded by stubs, shorting pins, and a split-ring resonator (SRR) metamaterial structure to increase the antenna’s gain and enhance the bandwidth (BW) towards both the lower and higher end of the working BW. The antenna’s performance in soil inspection is studied in terms of the operating frequency range, different types of soil, different distances (e.g., 50 cm) between the antenna arrays and soil, S-parameters, and gain. Following this, the antenna’s ability to find a metallic target in the soil is tested, considering different array numbers, multi-targets, and locations. The antenna is designed on a thin layer of economic polytetrafluoroethylene (PTFE) substrate with dimensions 50 × 39 × 0.508 mm3 and works in the frequency range 1.9–9.2 GHz. In addition, two more resonances at 0.9 and 1.8 GHz are also achieved; hence, the antenna works for more than two application bands, such as the ISM- and L-bands. The measurement results validated excellent agreement with the simulated results. Furthermore, the recommended antenna offering a high gain of about 10.8 dBi and maximum efficiency above 97% proved able to discriminate between hidden objects and even recognize their shapes. Moreover, the reconstructed images show that the antenna can detect an object in the soil at any location.

Game Technologies and Gamification Techniques in Teaching English: An Analysis of Pedagogical Experience

Game technologies (including computer-based ones) applied for teaching English have long occupied an important and permanent place in the structure of the lesson. They are very helpful in developing intercultural communicative competences as well as increasing motivation and self-organization. The purpose of this study is to objectively show how game methods of work and gamification techniques are actually implemented by modern teachers in English classes in both full-time and distance education. The theoretical significance of the study lies in the analysis of empirical psychological and pedagogical research on the problems of teaching English by both domestic and foreign scientists, on the basis of which the authors have compiled a questionnaire to summarize the experience of English teachers, classify electronic games and gamification techniques in the classroom. The study sample consisted of 48 English teachers with teaching experience from 1 to 13 years, the age range of their students being from 3 to 60 years old. The teachers used the questionnaire to analyze their experience in using gamification techniques and game technologies both in face-to-face and online lessons. All the teachers used game methods of work and gamification techniques in their classes regardless of the students’ age. The games that the teachers included in their lessons could be conveniently classified into didactic games (used by two-thirds of the teachers), multiplayer cooperation games (included by every fifth teacher) and hidden object games with a non-linear plot (used in class by a quarter of the teachers in the sample). Most of the teachers who used the gamification techniques noted a steady increase in students’ motivation in the classroom. The practical significance of the research results lies in the possibility of building an individual trajectory of self-education and professional development of English language teachers both in basic and in supplementary education. The generalized practical experience of the teachers who participated in the study can be used as the basis for methodological and pedagogical recommendations for improving informational and communicational competences and pedagogical skills of English language teachers.

“Yay! Yuck!” toddlers use others’ emotional responses to reason about hidden objects

Young children show sensitivity to others’ emotions, discriminating between facial expressions and using them to help guide their behavior. Beyond providing information about how others are feeling, emotional expressions also can support inferences about the non-social world. Here, in four experiments, we investigated 18- to 28-month-old children’s ability to use others’ emotional responses to reason about physical objects. We found that 24- to 26-month-old children successfully used an agent’s incongruent emotional responses (“Yay! Yuck!”), but not congruent emotional responses (“Yay! Wow!”) to infer the presence of multiple hidden objects (Experiment 1). When two different agents produced the incongruent emotional responses, children did not infer that multiple objects must be present (Experiment 2), implicating early recognition that different people can have different emotional reactions towards the same entity. Younger, 20-month-old children failed to use incongruent emotional responses to make inferences about hidden objects (Experiment 3), although they succeeded at using contrasting words in an otherwise identical task (“A blick! A fep!”; Experiment 4). These results show that young children can use other people’s emotional responses to reason about the physical world—an ability that develops in the second year of life.

Synthetic Spatial Foraging With Active Inference in a Geocaching Task.

Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we present a deep Active Inference model of goal-directed behavior, and the accompanying belief updating. Active Inference rests upon optimizing Bayesian beliefs to maximize model evidence or marginal likelihood. Bayesian beliefs are probability distributions over the causes of observable outcomes. These causes include an agent’s actions, which enables one to treat planning as inference. We use simulations of a geocaching task to elucidate the belief updating—that underwrites spatial foraging—and the associated behavioral and neurophysiological responses. In a geocaching task, the aim is to find hidden objects in the environment using spatial coordinates. Here, synthetic agents learn about the environment via inference and learning (e.g., learning about the likelihoods of outcomes given latent states) to reach a target location, and then forage locally to discover the hidden object that offers clues for the next location.

CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving

In the context of autonomous driving, the existing semantic segmentation concept strongly supports on-road driving where hard inter-class boundaries are enforced and objects can be categorized based on their visible structures with high confidence. Due to the well-structured nature of typical on-road scenes, current road extraction processes are largely successful and most types of vehicles are able to traverse through the area that is detected as road. However, the off-road driving domain has many additional uncertainties such as uneven terrain structure, positive and negative obstacles, ditches, quagmires, hidden objects, etc. making it very unstructured. Traversing through such unstructured area is constrained by a vehicle’s type and its capability. Therefore, an alternative approach to segmentation of the off-road driving trail is required that supports consideration of the vehicle type in a way that is not considered in state-of-the-art on-road segmentation approaches. To overcome this limitation and facilitate the path extraction in the off-road driving domain, we propose traversability concept and corresponding dataset which is based on the notion that the driving trails should be finely resolved into different sub-trails and areas corresponding to the capability of different vehicle classes in order to achieve safe traversal. Based on this, we consider three different classes of vehicles (sedan, pickup, and off-road) and label the images corresponding to the traversing capability of those vehicles. So the proposed dataset facilitates the segmentation of off-road driving trail into three regions based on the nature of the driving area and vehicle capability. We call this dataset as CaT (CAVS Traversability, where CAVS stands for Center for Advanced Vehicular Systems) dataset and is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.cavs.msstate.edu/resources/downloads/CaT/CaT.tar.gz</uri> .

Fast and Robust Capacitive Imaging of Cylindrical Non-Metallic Media

In this paper, a unique approach to the imaging of non-metallic media using capacitive sensing is presented. By using customized sensor plates in single-ended and differential configurations, responses to hidden objects can be captured over a cylindrical aperture surrounding the inspected medium. Then, by processing the acquired data using a novel imaging technique based on the convolution theory, Fourier and inverse Fourier transforms, and exact low resolution electromagnetic tomography (eLORETA), images are reconstructed over multiple radial depths using the acquired sensor data. Imaging hidden objects over multiple depths has wide range of applications, from biomedical imaging to nondestructive testing of the materials. Performance of the proposed imaging technique is demonstrated via experimental results.

Further Evidence That People Rely on Egocentric Information to Guide a Cursor to a Visible Target.

Everyday movements are guided by objects’ positions relative to other items in the scene (allocentric information) as well as by objects’ positions relative to oneself (egocentric information). Allocentric information can guide movements to the remembered positions of hidden objects, but is it also used when the object remains visible? To stimulate the use of allocentric information, the position of the participant’s finger controlled the velocity of a cursor that they used to intercept moving targets, so there was no one-to-one mapping between egocentric positions of the hand and cursor. We evaluated whether participants relied on allocentric information by shifting all task-relevant items simultaneously leaving their allocentric relationships unchanged. If participants rely on allocentric information they should not respond to this perturbation. However, they did. They responded in accordance with their responses to each item shifting independently, supporting the idea that fast guidance of ongoing movements primarily relies on egocentric information.

Accurate Imaging of Wide Beam Active Millimeter Wave Based on Angular Spectrum Theory and Simulation Verification

Due to the fact that a millimeter-wave (MMW) has a strong ability to penetrate clothing, MMW holographic imaging technology can conduct a non-contact inspection of the human body’s surface. In recent years, personnel surveillance systems utilizing MMW holographic imaging technology has achieved rapid progress. However, limited by MMW holographic imaging’s image quality, the existing imaging technology cannot accurately detect whether the human body carries hidden objects. Additionally, real-time inspection requirements cannot be practically satisfied, and the system cost is relatively high. In this paper, a reconstruction algorithm with enhanced imaging quality, which can solve the problem of spherical wave attenuation with distance, making imaging results more accurate. The sampling conditions and imaging resolution are simulated and analyzed, which verify the azimuth resolution. Furthermore, the antenna beam’s holographic imaging simulation is optimized, effectively improving the quality of the reconstructed image. The proposed scheme provides theoretical support for determining antenna step and scanning aperture size in engineering and have theoretical guiding significance for improving the image quality of millimeter-wave holography and reducing system cost.

Shadow playing with Romanian 5-year-olds. Epistemic adverbs are a kind of magic!

The current paper employs a novel Shadow Play Paradigm to investigate the semantic knowledge and pragmatic ability of Romanian 5-year-olds with respect to the epistemic adverbs poate ‘maybe’ and sigur ‘certainly’. The paradigm is an improved version of the Hidden Object Paradigm, where, instead of merely looking at an inaccessible entity, participants can now infer the presence of the entity on the basis of evidence (a shadow, as well as a specific sound). We argue that Romanian children as young as 5 are able to derive implicatures with epistemic adverbs at an almost adult-like level. However, they exhibit the tendency to accept overly strong statements (i.e., statements where a certainty adverb is wrongly used instead of a possibility adverb) as optimal to a much higher degree than adults. This can be explained as a cognitive/communicative strategy to reduce multiple alternatives to a single one in cases of uncertainty.