Accurate alignment of real-world object poses with their virtual counterparts using sensors, e.g. cameras, is essential for consistent interaction in mixed-reality systems. However, objects can undergo abrupt, untracked movements during periods when a tracking system is inactive, e.g., overnight, causing stored pose records to become inconsistent with the real scene and breaking user interaction in the virtual environment. Off-the-shelf 3D reconstruction networks such as MASt3R (Matching and Stereo 3D Reconstruction) method provide metrically scaled 3D point maps and pixel correspondences, but they are trained on static scenes and therefore fail to produce reliable object correspondences when the object has moved. We propose a robust pipeline that combines MASt3R's metrically scaled 3D outputs with a background-based alignment strategy to recover and apply the true pose change of moved objects. Our method first segments foreground and background and extracts 3D background point sets for a reference day and a current day. An affine transformation between these background point sets is estimated via a standard registration technique and used to express the current-day object 3D coordinates in the reference coordinate frame. Within that unified frame we compute the object pose change and apply the resulting transform to the virtual object, restoring real-virtual consistency. Experiments on real scenes demonstrate that the proposed approach reliably corrects pose misalignments introduced during inactive periods and substantially improves over applying MASt3R alone, thereby enabling restored and consistent user interaction in the virtual environment.

The complexity of modern battlefields demands advanced military training systems that prepare forces for realistic scenarios. Even though traditional training methods are effective, they are costly and time consuming and are associated with safety risks. Virtual training systems offer a safer and more cost-effective alternative; however, current solutions often compromise realism because of the need for multiple sensors and wearable devices that can diminish immersion. To address these limitations, we propose a marker-based, adaptive, virtual military training system (MAVMTS) that enhances realism by using a minimal set of fiducial markers and multiview cameras to estimate the trainee's posture and weapon orientation without cumbersome wearables. The system integrates action recognition to generate responsive virtual adversaries, thereby creating dynamic and immersive training environments. MAVMTS reduces considerably the equipment burden and enhances the realism of virtual military training, thereby offering a more effective solution for preparing personnel for modern warfare.

The rise of mobile applications in Virtual Reality (VR) and Augmented Reality (AR), particularly those using Head-Mounted Displays (HMDs), underscores the need to understand egocentric perspectives. This paper addresses the room-level localization challenge-identifying the room a user is in from an egocentric image-by framing it as a classification problem with a deep neural network. While deep learning has achieved remarkable success in conventional image classification, room classification from egocentric images introduces unique challenges due to variability and ambiguity in the user's perspective. Unlike typical datasets that provide clear visual data, egocentric views often lack sufficient detail, making uncertainty estimation crucial for achieving accurate results. Our approach not only advances egocentric localization but also holds potential for improving navigation and context-aware applications in AR/VR environments. We propose a novel strategy for uncertainty estimation and validate it with a custom dataset. Experimental results reveal significant performance improvements, achieving near-perfect accuracy by effectively managing ambiguous samples.

Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site - which were impossible to organise during 2021 and 2022 - in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations; ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site-which were impossible to organize during 2021 and 2022-in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.

Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in a relative sense with respect to the initial position. The size, weight, and power consumption of micromechanical systems (MEMS) embedded into smartphones are remarkably low, making them appropriate for localization and positioning. Traditional pedestrian PDR methods predict position and orientation using stride length and continuous integration of acceleration in step and heading system (SHS)-based PDR and inertial navigation system (INS)-PDR, respectively. However, these two approaches provide accumulations of error and do not effectively leverage the inertial measurement unit (IMU) sequences. The PDR navigation solution relays on the standard of the MEMS, which yields PDR with the acceleration and angular velocity from the accelerometer and gyroscope, respectively. However, low-cost small MEMSs endure enormous error sources such as bias and noise. Hence, MEMS assessments lead to navigation solution drifts when utilized as inputs to the PDR. As a consequence, numerous methods have been proposed to mitigate and model the errors related to MEMS. Deep learning-based dead reckoning algorithms are provided to address aforementioned issues owing to the end-to-end learning framework. This paper proposes a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM)-based inertial PDR system that extracts inertial measurement units (IMU) sequence features. The end-to-end learning framework is introduced to leverage the efficiency of low-cost MEMS because data-driven solutions provide more complete knowledge of the ever-increasing data volume and computational power over the filtering model approach. A CNN-LSTM model was employed to capture local spatial and temporal features. Experiments conducted on odometry datasets collected from multi-sensor backpack devices demonstrated that the proposed architecture outperformed previous traditional PDR methods, demonstrating that the root mean square error (RMSE) for the best user was 0.52 m. On the handheld smartphone-only dataset the best achieved R2 metric was 0.49.

본 논문은 전투원들이 불규칙적이고 동적인 움직임으로 사전정보 없이 처음 진입하는 건물이나 지하벙커, GNSS-denied 된 열악한 전장 상황하에 전장 상황인식, 위협 판단 및 지휘 결심을 지원함으로서 전투병의 인지적 부담을 경감시키는 멀티에이전트 기반 유·무인 협업 시스템을 제안한다. 본 유·무인 협업 시스템은 세 가지 핵심 기술로 이루어진다. 전장 상황에서 전투병의 전장 상황인식, 위협 판단 및 지휘 결심을 지원하는 볼타입 로봇의 브레인 역할을 수행하는 협업형 뉴로에이전트 기술 및 전투원 주변 객체정보, 시멘틱 정보 및 기하학정보를 활용하여 전장 환경을 이해하기 위한 극소량 데이터의 메타학습을 이용한 열악한 환경에 강인한 전술맵 생성 기술, 협업형 뉴로에이전트로부터 개별 처리된 멀티모달 지식 정보를 융합하여 실시간으로 전투병 및 지휘관의 의사결심을 지원하는 실시간 전장 모델 학습 기반 전장상황인지 기술로 구성된다. 제안된 핵심기술은 최신 데이터 과학에 기반한 인공지능 기술로 구현되며 이를 통해 전투원의 생존성을 높일 수 있는 작전지휘통제 체계 구축을 가능하게 한다.

Most of enemies in battlefields are not visible due to cover and concealment, and it yields fear and a weakened combat power for allies. In order to overcome it, we present a new approach for visualizing hidden enemies in battlefields to enhance cognition ability and survival for soldiers. Our method is composed of two separate sub-task networks. One is an efficient real-time panoptic segmentation network based on YOLACT [1] to find hidden enemies as well as to understand scenes from the viewpoint of soldiers. The other is an image completion network to reconstruct occluded parts of enemies which is guided by the panoptic segmentation networks. Our experiments on the Cityscapes benchmarks show that the proposed panoptic segmentation network achieves almost realtime speed without significant performance drops. We also demonstrate qualitative results of our segmentation-guided image completion method successfully on a dataset constructed from images of the Battlefield4 game.

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m2 outdoors and and 6000 m2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

There are various problems in society that can be solved by IT technology, and we would like to provide a technique to mitigate safety incidents and accidents that occur continuously in military life. As the current use of smart devices by soldiers has had a positive effect, it becomes possible to provide specialized care services such as personalized health care, psychology care, physical strength check-up and feedback by AI coaches, based on the requirements of the soldiers. These services can provide a reliable and safe environment for personnel fulfilling the duty of defense. However, in order to figure out the psychological factors of problems such as barracks accidents caused by special environments of soldiers, those services need an AI technology with the level of explainable Artificial Intelligence (XAI) or Artificial Wisdom (AW) beyond handling a psychological feedback. Therefore, in this paper, we propose IT platform services that are able to support the needs currently raised to deal with problems that arise in the mid- to long-term life of the specific group as universal social welfare issues.

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75 th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future.

Although there are many commercial products and services advertising their capability of indoor navigation using smartphone, still the service coverage is highly restricted to the specific area due to tremendous effort on pre-survey and maintenance on positioning resources such as radio map of Wi-Fi signals. Most of indoor positioning is dependent on the installed facilities such as Wi-Fi APs and BLE beacons in service area. Without pre-surveyed information such as fingerprint database, one can not localize himself at the first visiting site.

Standard treatment for locally advanced (stage III-IV) head and neck squamous cell cancer (LA-HNSCC) is concurrent chemoradiation therapy (CCRT) with cisplatin 100 mg/m 2 every 3 weeks. For medically unfit patients susceptible to treatment-related adverse events, low-dose weekly cisplatin (30–40 mg/m 2) can be used as an alternative. In this study, we retrospectively compared the therapeutic outcomes of low-dose weekly cisplatin regimen and standard regimen in CCRT for LA-HNSCC.

Standard treatment for locally advanced (stage III-IV) head and neck squamous cell cancer (LA-HNSCC) is concurrent chemoradiation therapy (CCRT) with cisplatin 100mg/m(2) every 3 weeks. For medically unfit patients susceptible to treatment-related adverse events, low-dose weekly cisplatin (30-40mg/m(2)) can be used as an alternative. In this study, we retrospectively compared the therapeutic outcomes of low-dose weekly cisplatin regimen and standard regimen in CCRT for LA-HNSCC.The medical records of histologically confirmed LA-HNSCC patients were retrospectively reviewed from January 1, 2007 to December 31, 2012. Patients who were treated with CCRT as initial treatment were included.Among 220 patients eligible, 65 (29.5%) were treated with cisplatin dosing schedule of 100mg/m(2) every 3 weeks and 155 (70.5%) with 30 to 40mg/m(2) weekly. The overall response rate in 3-weekly group was 92.3% and did not differ from that in weekly group (91.0%). The median progression-free survival of the weekly group was not attained but was not significantly different from that of 3-weekly group (50.7 months, 95% confidence interval [CI] 42.2-59.1 months) (P=.81). Also, the median overcall survival did not differ significantly between 2 groups (P=.34).In the present study, low-dose weekly cisplatin showed therapeutic outcomes comparable to standard-dose cisplatin in CCRT for LA-HNSCC. Prospective comparison of standard-dose three-weekly and low-dose weekly cisplatin is warranted.

스마트 단말기는 업무나 여가 활동을 위한 차량 또는 보행 이동에 있어서 필수적인 도구가 되었다. 특히 처음 가는 지역이나 교통 체증 상황에서의 길 안내는 이용자의 만족도가 높게 발전되었다. 그런데 일반인을 위한 기능의 유용성이 있어도 때로 장년층, 장애인, 임산부, 일시적 상해자 등 교통 약자에 대해서는 다른 관점의 요구 사항이 존재한다. 또한 실외뿐만 아니라 실내 공간에서의 길 안내도 오래 머무는 방향으로 지향하므로 더욱 중요해지고 있다. 일반적으로 교통 약자는 사회적 약자이자 경제적 약자의 가능성이 크다. 본 논문에서는 정밀 측위 기술 분야에서 교통 약자에게 요구되는 보편적 요구사항을 도출하고 고령화 및 복지 사회에서 대중이 대중을 지원하는 융합 기술에 기반한 실제적이고 경제적인 스마트 길 안내 시스템의 구조와 기법을 제안하고자 한다.

To overcome the limitations of the conventional motion tracking and localisation methods: environmental constraints of the infra-based approaches and long-term inaccuracy of the wearable sensor-based approaches, an algorithm for simultaneous motion tracking and localisation (SMaL) of a person, namely multiple inertial measurement unit (IMU)-based SMaL (MIbS), is proposed. A filtering method for the multiple IMUs/depth camera integration (MDI) is also proposed to add long-term localisation accuracy to the MIbS. The performance of the proposed MIbS and MDI is verified experimentally. The experimental results show that the proposed MIbS can track the motion of the person with the wearable sensor system accurately, and can localise the person on the local coordinate frame of the testbed. The encouraging fact is that the localisation errors caused by the MIbS grow slowly with time. Also, the growing localisation errors can be compensated by the MDI.

Manage 2015;61:28-33. 10. Khodosovskiĭ MN, Zinchuk VV. Erythropoietin influence on the blood oxygen transport and prooxidant-antioxidant state during hepatic ischemia-reperfusion. Ross Fiziol Zh Im I M Sechenova 2014;100:592-601. 11. Kalantzi M, Kalliakmani P, Papachristou E, et al. Parameters influencing blood erythropoietin levels of renal transplant recipients during the early post-transplantation period. Transplant Proc 2014;46:3179-82. 12. Hernández-Navarrete LS, Hernández-Jiménez JD, JiménezLópez LA, Budar-Fernández LF, Méndez-López MT, MartínezMier G. Experience in kidney transplantation without blood transfusion: kidney transplantation transfusion-free in Jehovah's Witnesses. First communication in Mexico. Cir Cir 2013;81: 450-3. 13. Zabaneh R, Roger SD, El-Shahawy M, et al. Peginesatide to manage anemia in chronic kidney disease patients on peritoneal dialysis. Perit Dial Int 2015;35:481-9. 14. Bartnicki P, Kowalczyk M, Rysz J. The influence of the pleiotropic action of erythropoietin and its derivatives on nephroprotection. Med Sci Monit 2013;19:599-605. 15. Tsompos C, Panoulis C, Toutouzas K, Triantafyllou A, Zografos GC, Papalois A. Comparison of the attenuating capacities of erythropoietin and U-74389G concerning blood platelet counts. J Sci Achiev 2017;2:18-21.

In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.

Smart devices are an essential navigating tool on indoor and outdoor environments for work or leisure activities. Specially, it's very useful in first visited area and on traffic congestion. However, there are different requirements for the elderly, physically impaired people, blind and woman in pregnant, and so on. Moreover, indoor positioning is more important in urban living. Therefore, we investigate how to enhance the mobility and accessibility of ordinary people as many as possible. This paper proposes the architecture of smart platform and devices including automatic updates based various sensor data and images from all types of pedestrian.

This paper proposes to verify the performance & error analysis of pedestrian indoor navigation system using commercially available low-cost inertial sensors. This self-contained approach employs Euler for attitude representation, where the estimation problem is formulated as an Extended Kalman filter (EKF) for INS strap down mechanization equations. The algorithm outputs are the foot kinematic parameters, which include foot orientation, position, velocity, acceleration, and stance phase. The approach is based on a zero-velocity update (ZUPT), Zero angular rate update (ZARU), Heuristic heading drift reduction (HDR) algorithms. The main contribution of the paper is to compare and analyse the heading drift reduction algorithms on Kalman-based IEZ platform and estimating the return position error. Orientation is then determined from the foot's initialized from accelerometer sensor information. Finally, we evaluated using experiments, including both short distance walking with different patterns and long distance walking performed in indoor.