Search Results (8)

Visual impairment or even loss seriously affects quality of life. Benefited by the rapid development of sound/laser detection, Global Positioning System (GPS)/Beidou positioning, machine vision and other technologies, the quality of life of blind people is expected to be improved through visual substitution technology. The existing visual substitution devices still have limitations in terms of safety, robustness, and ease of operation. The remote companion system developed here fully utilizes multimodal navigation and remote communication technologies, and the positioning and interaction functions of commercial mobile phones. Together with the accumulated judgment of backend personnel, it can provide real-time, safe, and reliable navigation services for blind people, helping them complete daily activities such as independent travel, circulation, and shopping. The practical results show that the system not only has strong operability and is easy to use, but also can provide users with a strong sense of security and companionship, making it suitable for promotion. In the future, this system can also be promoted for other vulnerable groups such as the elderly. Full article

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age. Full article

Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR. Full article

This paper is the companion paper of Remote Microgrids for Energy Access in Indonesia “Part I: scaling and sustainability challenges and a technology outlook”. This part II investigates the issues of photovoltaic (PV) systems with respect to the planning, design, and operation, and maintenance phases in microgrids in Indonesia. The technology outlooks are also included as PV has an important role in providing electricity in the underdeveloped, isolated, and border areas. The data in this paper are from PV microgrids located in Maluku and North Maluku, which are two provinces where there is barely any grid connection available and thus very dependent on remote microgrids. The data are obtained from interviews with Perusahaan Listrik Negara (PLN) and NZMATES, which are an Indonesian utility company and a program for supporting role for the PV systems in Maluku funded by New Zealand respectively. Common issues with respect to reliability and sustainability are identified based on the provided data. Advanced technologies to increase reliability and sustainability are also presented in this paper as a technology outlook. Among these solutions are online monitoring systems, PV and battery lifetime estimation, load forecasting strategies, and PV inverters technology. Full article

Recently, rapid advances in radio detection and ranging (radar) technology applications have been implemented in various fields. In particular, micro-Doppler radar has been widely developed to perform certain tasks, such as detection of buried victims in natural disaster, drone system detection, and classification of humans and animals. Further, micro-Doppler radar can also be implemented in medical applications for remote monitoring and examination. This paper proposes a human respiration rate detection system using micro-Doppler radar with quadrature architecture in the industrial, scientific, and medical (ISM) frequency of 5.8 GHz. We use a mathematical model of human breathing to further explore any insights into signal processes in the radar. The experimental system is designed using the USRP B200 mini-module as the main component of the radar and the Vivaldi antennas working at 5.8 GHz. The radar system is integrated directly with the GNU Radio Companion software as the processing part. Using a frequency of 5.8 GHz and USRP output power of 0.33 mW, our proposed method was able to detect the respiration rate at a distance of 2 m or less with acceptable error. In addition, the radar system could differentiate different frequency rates for different targets, demonstrating that it is highly sensitive. We also emphasize that the designed radar system can be used as a portable device which offers flexibility to be used anytime and anywhere. Full article

Companion animal management in Australian remote Aboriginal communities (rAcs) is a complex problem with multiple stakeholders involved, with differing needs, knowledge, power and resources. The Comm4Unity (Cycle of Multiple Methods for Unity—For Community) approach was designed to address such problems. This study represents the second step of the Comm4Unity framework, where a causal loop analysis (CLA) was adapted and tested as a tool to address the issue of dog overpopulation in Wurrumiyanga, and in particular the systemic causes of the problem and necessary transformational management solutions. Ten focus group discussions (FGDs) were held amongst three of the four stakeholder groups identified during the first step in the analysis. The CLA identified 13 positive feedback loops, which drive vicious cycles and perpetuate the dog overpopulation issue. All three groups agreed and developed 22 solutions to address the causes of dog overpopulation. Despite the differences in the framings of the three groups, “training” and “education” were both the top priority solutions for all three groups. The majority of the solutions discussed by the groups were not only transformational but also social, requiring collaboration. This study was successful in so far as transformational actions were co-developed by all FGDs, which may have also built capacity and agency amongst the local community to implement them as a cohesive group. Full article

Companion animal management in Australian remote Aboriginal communities (rAcs) is a complex problem, with multiple stakeholders involved with differing needs, knowledge, power and resources. We present our CoMM4Unity approach, a participatory systemic action research process designed to address such problems. In the first step, frame analysis is used to analyse stakeholders’ perspectives, knowledge types and power dynamics to determine their relative roles in animal management. Twenty individuals were interviewed from stakeholder groups involved in animal management in the remote, island rAc of Wurrumiyanga, Tiwi Islands. Frame analysis indicated that stakeholders aligned into four groups with distinct identity frames, knowledge types and power frames: Indigenous Locals, Indigenous Rangers, Non-Indigenous Locals and Animal Managers. All four groups shared overlapping perceptions about companion animals in Wurrumiyanga, and agreed that dog overpopulation was the primary issue. However, the groups differed in their strength of opinions about how dogs should be managed. Therefore, the situation is not one of diametrically opposing frames but more a misalignment of goals and values. Our application showed that frame analysis can reveal subtle variations in stakeholder groups’ identities, goals and values, and hence how they prioritise management measures. Full article

Warm water masses circulating at depth off the coast of Greenland play an important role in controlling rates of mass loss from the Greenland Ice Sheet through feedbacks associated with the melting of marine glacier termini. The ability of these warm waters to reach glacier termini is strongly controlled by fjord bathymetry, which was unmapped for the majority of Greenland’s fjords until recently. In response to the need for bathymetric measurements in previously uncharted areas, we developed two companion methods to infer fjord bathymetry using icebergs as depth sounders. The main premise of our methods centers around the idea that deep-drafted icebergs will become stranded in shallow water such that estimates of iceberg surface elevation can be used to infer draft, and thus water depth, under the assumption of hydrostatic equilibrium. When and where available, surface elevations of icebergs stranded on bathymetric highs were extracted from digital elevation models (DEMs) and converted to estimates of iceberg draft. To expand the spatial coverage of our inferred water depths beyond the DEM footprints, we used the DEMs to construct characteristic depth–width ratios and then inferred depths from satellite imagery-derived iceberg widths. We tested and applied the methods in two fjord systems in western Greenland with partially constrained bathymetry, Ilulissat Isfjord and Naajarsuit Fjord, to demonstrate their utility for inferring bathymetry using remote sensing datasets. Our results show that while the uncertainties associated with the methods are high (up to ±93 m), they provide critical first-order constraints on fjord bathymetry. Full article