Towards Tangible Cultural Heritage Experiences—Enriching VR-based Object Inspection with Haptic Feedback

VR/AR technology is a key enabler for new ways of immersively experiencing cultural heritage artifacts based on their virtual counterparts obtained from a digitization process. In this article, we focus on enriching VR-based object inspection by additional haptic feedback, thereby creating tangible cultural heritage experiences. For this purpose, we present an approach for interactive and collaborative VR-based object inspection and annotation. Our system supports high-quality 3D models with accurate reflectance characteristics while additionally providing haptic feedback regarding shape features of the object based on a 3D printed replica. The digital object model in terms of a printable representation of the geometry as well as reflectance characteristics are stored in a compact and streamable representation on a central server, which streams the data to remotely connected users/clients. The latter can jointly perform an interactive inspection of the object in VR with additional haptic feedback through the 3D printed replica. Evaluations regarding system performance, visual quality of the considered models, as well as insights from a user study indicate an improved interaction, assessment, and experience of the considered objects.

Layered and object-based game semantics

Large-scale software verification relies critically on the use of compositional languages, semantic models, specifications, and verification techniques. Recent work on certified abstraction layers synthesizes game semantics, the refinement calculus, and algebraic effects to enable the composition of heterogeneous components into larger certified systems. However, in existing models of certified abstraction layers, compositionality is restricted by the lack of encapsulation of state. In this paper, we present a novel game model for certified abstraction layers where the semantics of layer interfaces and implementations are defined solely based on their observable behaviors. Our key idea is to leverage Reddy's pioneer work on modeling the semantics of imperative languages not as functions on global states but as objects with their observable behaviors. We show that a layer interface can be modeled as an object type (i.e., a layer signature) plus an object strategy. A layer implementation is then essentially a regular map, in the sense of Reddy, from an object with the underlay signature to that with the overlay signature. A layer implementation is certified when its composition with the underlay object strategy implements the overlay object strategy. We also describe an extension that allows for non-determinism in layer interfaces. After formulating layer implementations as regular maps between object spaces, we move to concurrency and design a notion of concurrent object space, where sequential traces may be identified modulo permutation of independent operations. We show how to express protected shared object concurrency, and a ticket lock implementation, in a simple model based on regular maps between concurrent object spaces.

Object-Based Geomorphological Mapping: Application on an Alpine Deep-Seated Gravitational Slope Deformation Contest (Germanasca Valley, Western Alps—Italy)

This research reports the use of a new method of geomorphological mapping in GIS environments, using a full-coverage, object-based method, following the guidelines of the new geomorphological legend proposed by ISPRA–AIGEO–CNG. This methodology is applied to a tributary valley of the Germanasca Valley, shaped into calcschist and greenschist, of the Piedmont Zone (Penninic Domain, Western Alps). The investigated sector is extensively affected by dep-seated gravitational slope deformation (DSGSD) that strongly influences the geological setting and the geomorphological features of the area. The mapping of these gravitational landforms in a traditional way creates some difficulties, essentially connected to the high density of information in the same site and the impossibility of specifying the relationships between different elements. The use of the full-coverage, object-based method instead is advantageous in mapping gravitational evidence. In detail, it allows for the representation of various landforms in the same sector, and their relationships, specifying the size of landforms, and with the possibility of multiscale representation in the GIS environment; and, it can progressively be update with the development of knowledge. This research confirms that the use of the full-coverage, object-based method allows for better mapping of the geomorphological features of DSGSD evidence compared to classical representation.

Object-Based High-Rise Building Detection Using Morphological Building Index and Digital Map

High-rise buildings (HRBs) as modern and visually unique land use continue to increase due to urbanization. Therefore, large-scale monitoring of HRB is very important for urban planning and environmental protection. This paper performed object-based HRB detection using high-resolution satellite image and digital map. Three study areas were acquired from KOMPSAT-3A, KOMPSAT-3, and WorldView-3, and object-based HRB detection was performed using the direction according to relief displacement by satellite image. Object-based multiresolution segmentation images were generated, focusing on HRB in each satellite image, and then combined with pixel-based building detection results obtained from MBI through majority voting to derive object-based building detection results. After that, to remove objects misdetected by HRB, the direction between HRB in the polygon layer of the digital map HRB and the HRB in the object-based building detection result was calculated. It was confirmed that the direction between the two calculated using the centroid coordinates of each building object converged with the azimuth angle of the satellite image, and results outside the error range were removed from the object-based HRB results. The HRBs in satellite images were defined as reference data, and the performance of the results obtained through the proposed method was analyzed. In addition, to evaluate the efficiency of the proposed technique, it was confirmed that the proposed method provides relatively good performance compared to the results of object-based HRB detection using shadows.

Mental rotation of hands and objects in ageing and Parkinson’s disease: Implications for understanding motor imagery

Motor imagery (MI) supports motor learning and performance, having the potential to be a useful tool for neurorehabilitation. However, MI ability may be impacted by ageing and neurodegeneration, which could limit its therapeutic effectiveness. MI is often assessed through a hand laterality task (HLT), whereby laterality judgements are typically slower for hands presented at orientations corresponding to physically more difficult postures (a “biomechanical constraint” effect). Performance is also found to differ between back and palm views of the hand, suggesting the differential involvement of visual and sensorimotor strategies. While older adults are generally found to be slowed and show increased biomechanical effects, few studies have examined the effects of both ageing and Parkinson’s disease (PD).The present study compared healthy younger (YA), healthy older (OA) and PD groups on HLT performance from both palm and back views, as well as an object-based (letter) mental rotation task. OA and PD groups were slower than YA, particularly when judging laterality from the back view, and exhibited increased biomechanical constraint effects for the palm. While response times were generally similar between OA and PD groups, the PD group showed reduced accuracy in the back view. Moreover, object rotation was slower and less accurate only in the PD group. The results indicate that different mechanisms are involved in mental rotation of hands viewed from the back or palm, consistent with previous findings, and demonstrate particular effects of ageing and PD when judging the back view. Alongside findings from studies of explicit MI, this suggests a greater alteration of visual than kinaesthetic MI with ageing and neurodegeneration, with additional impairment of object-based visual imagery in PD. The findings are also discussed in relation to different perspectives in MI and the integration of visual and kinaesthetic representations.

Classification of Broadleaf Weeds Using a Combination of K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA)

Palm oil is one of the leading commodities in Indonesia. Oil palm yields can be influenced by several factors, one of which is proper weed control. Uncontrolled weeds can damage oil palm plantations. To be able to manage and control weeds, especially large leaf weeds, it is necessary to know the types of weeds. However, not all farmers have knowledge about the types of weeds. For that we need a system that can help identify broadleaf weeds based on leaf images using image processing. So this study aims to build a large leaf weed classification system using a combination of the K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA) algorithms. PCA is used as feature extraction based on the characteristics formed from each spatial property. PCA can be used to reduce and retain most of the relevant information from the original features according to the optimal criteria. The results of the information will then be used by KNN for learning by paying attention to the closest distance from the object. Based on the test results, the developed model is able to produce an accuracy of 90%. Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms can be used in the classification process properly. Accuracy results are strongly influenced by the amount of training data and test data as well as the quality of the image used.

Approbation of the stochastic group virus protection model

The article discusses the implementation in Java of the stochastic collaborative virus defense model developed within the framework of the Distributed Object-Based Stochastic Hybrid Systems (DOBSHS) model and its analysis. The goal of the work is to test the model in conditions close to the real world on the way to introducing its use in the practical environment. We propose a method of translating a system specification in the SHYMaude language, intended for the specification and analysis of DOBSHS models in the rewriting logic framework, into the corresponding Java implementation. The resulting Java system is deployed on virtual machines, the virus and the group virus alert system are modeled stochastically. To analyze the system we use several metrics, such as the saturation time of the virus propagation, the proportion of infected nodes upon reaching saturation and the maximal virus propagation speed. We use Monte Carlo method with the computation of confidence intervals to obtain estimates of the selected metrics. We perform analysis on the basis of the sigmoid virus propagation graph over time in the presence of the defense system. We implemented two versions of the system using two protocols for transmitting messages between nodes, TCP/IP and UDP. We measured the influence of the protocol type and the associated costs on the defense system effectiveness. To assess the potential of cost reduction associated with the use of different message transmission protocols, we performed analysis of the original DOBSHS model modified to model message transmission delays. We measured the influence of other model parameters important for the next steps towards the practical use of the model. To address the system scalability, we propose a hierarchical approach to the system design to make possible its use with a large number of nodes.

Assessing Spatiotemporal Built-up Dynamics in Chiang Mai City, Thailand using Entropy approach

The rates of urban dynamics affecting by industrialization, urban agglomeration, and large-scale migration turn its behaviour from monocentric to polycentric metropolitan resulting in unprecedented urban growth. Therefore, the present study incorporated an entropy-based approach to measure the degree of compactness and dispersiveness of urban development in Chiang Mai City. The Object-based machine learning was deployed for the image classifications with an overall accuracy above the minimum requirements (i.e., 90%) and kappa statistic of agreement above 0.85. The study reveals that Chiang Mai city has undergone urban development outskirts from the urban centre (CBD) and north and south-west direction from the CBD. A considerable increase in urban demographic and physical urban patches was observed in last 1998 to 2018. The research emphasized the significant role of Shannon Entropy to analyze the built-up growth supplemented by Remote Sensing and Geographic Information System (GIS) in respective zones and geographical directions.

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.