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The global rise in the elderly population, which presents challenges to healthcare systems owing to labor shortages in caregiving facilities, necessitates innovative solutions for elderly care services. Smart aging technologies such as robotic companions and digital home gadgets, offer a solution to these challenges by improving the elderly’s quality of life and assisting caregivers. However, limitations in data privacy, real-time processing, and reliability often hinder the effectiveness of the existing technologies. Among these, privacy concerns are a major barrier to ensuring user trust and ethical implementation. Therefore, this study proposes a more effective approach for smart aging through elderly activity monitoring that prioritizes data privacy. The proposed system utilizes stereo depth cameras to monitor the activities of the elderly. Data were collected from real-world environments with the participation of six elderly individuals from a care center and hospital. This system focuses on recognizing common daily actions of the elderly including sitting, standing, lying down, and seated in a wheelchair. Additionally, it recognizes transition states (in-between actions such as changing from sitting to standing) that are crucial for assessing balance issues. By integrating motion information with a deep-learning architecture, the system achieved a high accuracy of 99.42% in recognizing daily actions in real-time. This high accuracy was maintained even with minimal data from new environments through transfer learning, and the adaptability of this model ensured its potential for real-world applications. For intuitive interaction between the caregivers and the system, a user-friendly graphical interface (GUI) was also designed in the proposed approach.

This article proposes a novel planar array antenna for reconfigurable circular polarization (CP) with conical-beam radiation. The array consists of four radiating elements and a two-dimensional (2D) switchable feed network. Each element can switch CP using the proximity gap-coupled branches, augmented by a simple bias-decoupling mechanism through outstanding single-layer integration. Its symmetrical configuration also ensures consistent performance for the intended functionality during the switching operation. The initial optimization primarily focuses on CP reconfiguration in a single element, followed by the array design for conical-beam radiation. The effectiveness of this single-layer-based design technology is further validated through the fabrication of a $2\times 2$ array. The measured results reveal the 10-dB impedance bandwidth of 4%, 3-dB axial ratio (AR) bandwidth of 1.6%, and peak realized gain of 6.8 dBic. Also, the array successfully generates the conical-beam radiation with CP switching capability.

Pedestrian detection is fundamental in the realm of autonomous driving, relying on comprehensive datasets for accurate deep-learning methods. Detecting diverse pedestrian behaviors, including rare and near-accident cases, is necessary, however, there were limitations in the development of such specific datasets. Furthermore, involving humans in data collection is risky and raises privacy concerns since recording and storing data necessitates explicit consent from all participants. To tackle this challenge, we propose MetaPed, a synthetic pedestrian dataset generated from the Metaverse, where the avatars can represent a wide range of pedestrian behaviors without exposing real individuals to potential harm or privacy violations. Our Metaverse includes both programmed pedestrian behaviors and controllable avatar movements and interactions, ensuring a comprehensive and realistic representation of pedestrian actions. To validate and ensure the generalization capabilities of our synthetic dataset’s performance in real-world scenarios, we perform both cross-dataset and intra-dataset evaluations on comprehensive real-world datasets, including KITTI, Citypersons, and INRIA Person. Our experiments demonstrate that the network trained on our synthetic dataset exhibits robust generalization capabilities for unseen real-world situations and our dataset’s inclusion significantly enhanced the Average Precision (AP) and Average Recall (AR) of the pedestrian detection model. For cross-dataset validation, we generally observe improvements in both AP and AR with models trained on all datasets. The inclusion of our dataset also improves the intra-dataset validation of the INRIA Person dataset, where the AP and AR values increase from 0.958 to 0.988 and AR from 0.697 to 0.725, respectively. For Citypersons and KITTI datasets, the AP values increase from 0.630 to 0.639 and 0.745 to 0.752, respectively. The model training on our dataset achieved the highest AP and AR values across all test datasets and the second-highest AP observed when tested on Citypersons.

This paper proposes a novel design of a reconfigurable microstrip array antenna for circular polarization (CP) switching function. The proposed array is implemented in a $2\times 2$ arrangement using four patch elements and a feed network. The bias-isolation mechanism is an integral feature of this array design, and it is facilitated by the short-ended microstrip lines and planar magic-Ts. By this approach, both the radiating elements and feed structure ensure the straightforward RF-DC isolation method with no auxiliary passive components and filters. Also, with the superb integration of these two techniques, the entire array is viable in a single-layer configuration, featuring design freedom for a larger array. Furthermore, the CP switching function can simply be achieved by controlling the bias state of the diodes. To verify the proposed concept, prototype antennas are fabricated and tested. The measured results demonstrate that the proposed array can function as a CP reconfigurable antenna with simple bias-isolation method.

In this paper, we propose a deep Recurrent Neural Networks (RNNs) based on Gated Recurrent Unit (GRU) in a bidirectional manner (BGRU) for human identification from electrocardiogram (ECG) based biometrics, a classification task which aims to identify a subject from a given time-series sequential data. Despite having a major issue in traditional RNN networks which they learn representations from previous time sequences, bidirectional is designed to learn the representations from future time steps which enables for better understanding of context, and eliminate ambiguity. Moreover, GRU cell in RNNs deploys an update gate and a reset gate in a hidden state layer which is computationally efficient than a usual LSTM network due to the reduction of gates. The experimental results suggest that our proposed BGRU model, the combination of RNN with GRU cell unit in bidirectional manner, achieved a high classification accuracy of 98.55%. Various neural network architectures with different parameters are also evaluated for different approaches, including one-dimensional Convolutional Neural Network (1D-CNN), and traditional RNNs with LSTM and GRU for non-fiducial approach. The proposed models were evaluated with two publicly available datasets: ECG-ID Database (ECGID) and MIT-BIH Arrhythmia Database (MITDB). This paper is expected to demonstrate the feasibility and effectiveness of applying various deep learning approaches to biometric identification and also evaluate the effect of network performance on classification accuracy according to the changes in percentage of training dataset.

This paper presents a novel inductor-less switched capacitor (SC) DC-DC converter, which generates simultaneous dual-output voltages for implantable electronic devices. Present dual output converters are limited to fixed ratio gain, which degrade conversion efficiency when the input voltage changes. The proposed power converter offers both step-up and step-down conversion with 4-phase reconfigurable logic. With an input voltage of 1 V provided by photovoltaic (PV) cells, the proposed converter achieves step-up, step-down, and synchronised voltage conversions in four gain modes. These are 1.5 and 0.5 V for normal mode, 2 and 1 V for high mode, 2 V for double boost mode, as well as 3 and 2 V for super boost mode with the ripple variation of 14-59 mV. The converter circuit has been simulated in standard 0.18-μm CMOS technology and the results agree with the state-of-the-art SC converters. However, our proposed monolithically integrated PV powered circuit achieves a conversion efficiency of 85.26% and provides an extra flexibility in terms of gain, which is advantageous for future implantable applications that have a range of inputs. This research is, therefore, an important step in achieving truly autonomous implantable electronic devices.