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Recently, the number of dementia patients has been increasing due to the aging society. In Japan, a paper-based examination is the main-stream to measure the cognitive function of a subject, but these paper-based tests give much burden to not only patients but also evaluators like facility and medical staff. Therefore, it is necessary to develop a system that can automatically judge the degree of dementia progression, not to burden the doctor. Also, it is required to add play ability not to be a burden on the elderly. From this point of view, the authors developed a recreation game like a puzzle game. This system is easy to play for elderly people and is not a burden. Also, the question-answer is clear, so it is suitable for automatic judgment. We use the obtained features during recreation game to diagnose the degree of dementia progression. We committed the capability of machine learning techniques. Finally, we discussed that the collected features are sufficient to diagnose the degree of dementia progression.

One of the many challenges in advanced robotics is the autonomous exploration, recognition and manipulation of objects in cluttered unstructured workspaces. The problem is even more challenging when multiple heterogeneous robots with different tools or end-effectors are expected to perform complex collaborative missions, such as disassembly. Within this context, the aim of this work is to develop a framework enabling a robot to detect and localise objects in a workspace and share environment information with another robot, which subsequently performs a grasping operation. The motivation lies in merging point cloud data measured from multiple poses to enrich the representation of the workspace, and decomposing a part into generic primitive geometric features to allow efficient shape recognition in the semantic space. This allows easier integration with an ontological knowledge base for object searching using natural language input. To identify primitive geometrical characteristics and infer object types, this paper introduces a simple but efficient graph-based method, where the graph nodes represent elementary geometric shapes such as planes. The concept is demonstrated using two KUKA robots, where one is acting as the eye of the system, equipped with an RGB-D camera providing views from multiple angles, and the other one has a gripper for grasping, and is acting as a hand. Although the current paper uses basic components such as cubes and triangular blocks, the algorithm is interpretable, and can be extended with more complex shapes. The approach is demonstrated using wood blocks, which are employed to simulate disassembly in unstructured environments.

Globally used technical documents need to be understood by users who have different cultural and educational backgrounds. Documents should preferably be visual, but such documents are difficult to produce and maintain. In this article visualisations of technical information are categorised according to taxonomy of visual representation elements, which is then evaluated against cultural dependency, language dependency and required professional knowledge. Based on the categorisation, we propose how visual documents with less cultural dependencies can be produced so that they can be reconfigured if the product changes by applying single sourcing methods used in text document production.

In spite of the increasing awareness apparent in the literature regarding the selection of sustainable suppliers, there are limitations in incorporating the sustainable performance in terms of traditional, environmental and social aspects in supplier selection and order allocation. Thus, an integrated fuzzy TOPSIS-multi objective optimization (MOO) approach was developed to integrate sustainable performance into the evaluation and ranking of two-stage supplier selection in conjunction with the optimal order allocation in a meat supply chain. The sustainable performance of suppliers based on traditional, environmental and social criteria was evaluated by using fuzzy TOPSIS. The optimal quantity of products to be ordered from each supplier was determined through a development of multi-objective optimization model. To obtain a set of Pareto solutions derived from the multi-objective optimization model, the LP-metrics method was employed. The quality of the obtained Pareto solutions was evaluated using the global criterion approach aiming to select the final Pareto solution. To examine the applicability of the developed integrated fuzzy TOPSIS-multi-objective approach, a case study was applied.

Due to its high efficiency, the Iterative Closest Point (ICP) algorithm has become a popular choice in computer vision and robotics for the registration of point cloud data sets when the point-to-point correspondences are unknown. Its generalization for articulated structures, although possible through a joint optimization of all pose parameters, is challenging as it is necessary to solve a non-closed form. It also suffers heavily from the local minima problem. A number of proposed Articulated ICP (AICP) algorithms circumvent the problem of the non-closed form solution and offer an efficient alternative. However, they still exhibit an increased tendency, caused by the local minima, to converge to an incorrect pose. Typically, the above problem manifests itself after a transient disturbance in the convergence, such as an occlusion which causes an increase in the point-to-point association distances between the model and the data. In this paper, we propose an extension to the AICP algorithm that benefits from the efficiency of ICP as well as avoids its problems by using global pose optimization elements to guide the convergence process to the correct pose. The proposed approach is to merge adaptively the joint adjustments computed by AICP with the adjustments needed for a number of key points to reach their respective target positions, identified by a local feature descriptor search. Experiments show that the proposed Global-Local Articulated ICP algorithm exhibits improved robustness to transient disturbances, like occlusions, in comparison with the AICP algorithm.

Many wireless devices employ multi-rate techniques to improve network performance. However, despite the significant amount of research aimed at dynamically adjusting the transmission rate, the majority of this effort considers neither the competing nodes in wireless mesh networks nor the congestion in the nodes. This work employs distributed intelligent agents to observe the surrounding environment in order to dynamically adjust the individual node transmission rates. Reinforcement learning is employed to control the way each node updates its transmission rate based on the transmission rate of the adjacent node as well as the traffic load. This work is validated through extensive simulations that compare the proposed model with three of the most widely cited schemes. The results indicate significant improvement in system throughput.

Electroencephalography (EEG) offers a rich representation of human brain activity in the time domain. EEG would in many circumstances be the preferred technique for analysing brain activity, as it is less expensive and more practical to use than other modalities like functional Magnetic Resonance Imaging (fMRI), notably due to its size. However, its spatial resolution is limited, hampering its ability to characterise activity across spatially distributed brain networks. In comparison, functional Magnetic Resonance Imaging (fMRI) offers very good spatial resolution but the haemodynamic nature of the signal limits its temporal resolution to the order of seconds. A possible solution to this problem is to use both EEG and fMRI signals, but this approach would lead to the loss of convenience of EEG alone. We would like to bring in the advantages of fMRI signal into EEG assessment of brain state and brain responses without the necessity for the presence of the fMRI equipment on site. In this article, we propose a joint statistical model of fMRI/EEG signals and then exploit the learnt correlations to improve the results of signal processing of EEG on its own. We compare the performance of a Blind Source Separation (BSS) method on its own with one, which uses our joint EEG-fMRI model, and show the improvement in the precision of the source separation.

SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the initial demonstration platform. In this paper, experience gained while building the SRS system is presented. A main contribution of the paper is the SRS autonomous control framework. The framework is divided into two parts. First, it has an automatic task planner, which initialises actions on the symbolic level. The planner produces proactive robotic behaviours based on updated semantic knowledge. Second, it has an action executive for coordination actions at the level of sensing and actuation. The executive produces reactive behaviours in well-defined domains. The two parts are integrated by fuzzy logic based symbolic grounding. As a whole, they represent the framework for autonomous control. Based on the framework, several new components and user interfaces are integrated on top of COB's existing capabilities to enable robust fetch and carry in unstructured environments. The implementation strategy and results are discussed at the end of the paper.

Automated task planning for service robots faces great challenges in handling dynamic domestic environments. Classical methods in the Artificial Intelligence (AI) area mostly focus on relatively structured environments with fewer uncertainties. This work proposes a method to combine semantic knowledge representation with classical approaches in AI to build a flexible framework that can assist service robots in task planning at the high symbolic level. A semantic knowledge ontology is constructed for representing two main types of information: environmental description and robot primitive actions. Environmental knowledge is used to handle spatial uncertainties of particular objects. Primitive actions, which the robot can execute, are constructed based on a STRIPS-style structure, allowing a feasible solution (an action sequence) for a particular task to be created. With the Care-O-Bot (CoB) robot as the platform, we explain this work with a simple, but still challenging, scenario named “get a milk box”. A recursive back-trace search algorithm is introduced for task planning, where three main components are involved, namely primitive actions, world states, and mental actions. The feasibility of the work is demonstrated with the CoB in a simulated environment.

The study presented in this paper focuses on the preprocessing stage of image retrieval by proposing an ontology-based indexing approach which captures the meaning of image annotations by extracting the semantic importance of the words in them. The indexing algorithm is based on the classic vector-space model that is adapted by employing index weighting and a word sense disambiguation. It uses sets of Semantic DNA, extracted from a lexical ontology, to represent the images in a vector space. As discussed in the paper, the use of Semantic DNA in text-based image retrieval aims to overcome some of the major drawbacks of well known traditional approaches such as `bags of words' and term frequency-(TF) based indexing. The proposed approach is evaluated by comparing the indexing achieved using the proposed semantic algorithm with results obtained using a traditional TF-based indexing in vector space model (VSM) with singular value decomposition (SVD) technique. The experimental results show that the proposed ontology-based approach generates a better-quality index which captures the conceptual meaning of the image annotations