Light-detection-and-ranging (LiDAR) sensors are essential for the automated operation of network-connected vehicles in smart cities but are vulnerable to cyber-attacks. This paper introduces a system for anomaly detection that verifies the consistency of point cloud data across multiple LiDAR sensors. By comparing metrics derived from point cloud distributions under normal and potentially compromised conditions, the system effectively identifies anomalies, enhancing the security and reliability of network-connected LiDAR systems.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

In this paper, we describe our research on the possibility of automatically grouping textual information on behavioral and psychological symptoms of dementia stored in the Ninchisho Chienowa-net using the latest deep learning models for natural language processing. Ninchisho Chienowa-net is a web system that publishes the probability that coping methods for various symptoms occurring in patients with dementia will “go well” (probability of success) by collecting care information in the form of the posting from dementia caregivers and by grouping the posted care information according to the similarity of the care information. This grouping has been done manually by clinicians, but since the workload of clinicians has been increasing, we will verify the possibility of automating this process using deep learning models through experiments and investigate practical feasibility in Ninchisho Chienowa-net web system. As a result of the experiment, we constructed a learning model for automatic grouping with 20 epochs after multiple types of data preprocessing, and confirmed that the model can output the correct group within the top five with a probability of more than 90%. Furthermore, for the purpose of improving the performance of the automatic grouping model, we conducted data augmentation of text data using a translation service, and confirmed that we could output the correct groups within the top five with a probability of 98%.

This paper introduces the newest features and analysis of access logs of Ninchisho Chienowa-net, a web system that publishes the probability that coping methods for various symptoms occurring in patients with dementia will "go well" (probability of success). With an aim of reducing the burden on dementia caregivers, Ninchisho Chienowa-net not only publishes the probability of success of coping methods and information on dementia care, but is also equipped with features to collect experiences of coping methods for specific symptoms and to support the discovery of various coping methods. Since its launch in 2015, the site has seen a steady increase in the number of registered users and amount of access. Analysis of the access logs of Ninchisho Chienowa-net carried out in this study revealed that the access rate for the list of individual care topics is high and that it is necessary to prepare for large-scale access in the future.

Measuring the shear deformation of a finger pad during active hap tic exploration of materials is important for analysis of textural sensations and development of tactile texture displays. Thus far, there has been no general methods to measure such deformations for active touch. To create a sensor system, we have been developing a new method for estimating the deformation of a finger pad based on the skin deformation propagated to the radial side of a finger tip. In this study, in order to validate the method, we compared the deformation or its acceleration of finger pad estimated on the basis of the acceleration measured at the radial side and those estimated by the shear force applied to the finger pad while exploring a few types of materials. The estimation errors for the deformations at 40 -- 450 Hz were smaller than human discrimination thresholds, indicating that the accuracy of our method is satisfactory compared with human perceptual sensitivity.

In multimedia event detection, complex target events are extracted from a large set of consumer-generated videos taken in unconstrained environments. We devised a multimedia event detection method based on GMM supervectors and support vector machines (SVMs) using multiple features. A GMM supervector consists of the parameters of a Gaussian mixture model (GMM) for the distribution of local features extracted from a video clip. A GMM is regarded as an extension of the Bag-of-Words (BoW) to a probabilistic framework, and thus, it can be expected to be robust against the data insufficiency problem. This method outperformed previous methods including BoW in experiments using the dataset of the multimedia event detection task in TRECVID2010 and 2011.

We describe a parallel solution scheme for inverse dynamics, and its application to feedforward control of link mechanisms under various boundary conditions. The conditions include such cases as open- and closed-loops, and even one that continuously changes its form from an open- to a closed-loop. The dynamic equations conducted by generally used schemes such as the Newton-Euler method or the Lagrangian method, include interdependent variables between the constituting links which make it highly complicated to derive inverse dynamics of the closed-loop link mechanisms, or of the continuously transforming ones. The proposed scheme is developed by using the finite element method (FEM), and evaluates the entire system as a continuum. The system is subdivided into finite elements, and the nodal forces are evaluated by equations of motion in a matrix form. The joint torque in the system is then calculated by converting the obtained nodal forces. Therefore, information from the entire system can be handled in parallel, which makes it seamless in application to open/closed-loop or continuously transforming mechanisms. The control results of link mechanisms under various boundary conditions reveal the possibility of using the proposed solution scheme for feedforward control, independent of the system configuration of link mechanisms.

First and second-order approximations to the first passage time conditional probability density function of a stationary Gaussian process, with differentiable sample paths crossing a time-dependent boundary, are explicitly provided. In the limit of infinitely large time, our results coincide with the well-known results of Kac and Rice for the constant level-crossing problem.