Guidance System Research Articles

Diagnostic accuracy and safety of electromagnetic navigation transthoracic needle biopsy under moderate sedation for the diagnosis of peripheral pulmonary lesions.

Novel approaches using virtual computed tomography (CT) guidance, namely electromagnetic navigation transthoracic needle biopsy (EMN-TTNB), enable physicians to perform percutaneous lung biopsies. However, there are very few studies on the clinical experiences of EMN-TTNB, and in previous studies, the procedure was usually performed under deep sedation. This study aimed to determine the diagnostic accuracy and safety of EMN-TTNB under moderate sedation. We conducted a retrospective analysis of patients who underwent EMN-TTNB under moderate sedation between May 2021 and November 2022 at Hallym University Dongtan Sacred Heart Hospital in South Korea. Moderate sedation was achieved with midazolam injection in the bronchoscopy room using the Veran SPiNperc EM guidance system (Veran Medical, St Louis, MO, USA). Clinical data were collected by review of medical records, and diagnostic accuracy and safety were calculated. Thirty-two patients were enrolled (mean age 70.8±11.1 years); 56.3% were male. The mean size of the pulmonary lesions was 36.9±17.4 mm, and the median (interquartile range) distance from the pleura was 15.5 (0.0-30.0) mm. The diagnostic accuracy of EMN-TTNB was 75.0% (21/28), excluding four indeterminate cases. Fourteen patients (50.0%, 14/28) had true-positive and seven patients (25.0%, 7/28) had true-negative lesions. There were no severe adverse reactions such as pneumothorax, respiratory failure, or death, except one case of hemoptysis. EMN-TTNB under moderate sedation showed an acceptable diagnostic accuracy and good safety profile. The new technology allows physicians to perform percutaneous lung biopsies without the intervention of a radiologist or anesthesiologist.

A Coordinate-Regression-Based Deep Learning Model for Catheter Detection during Structural Heart Interventions

With a growing geriatric population estimated to triple by 2050, minimally invasive procedures that are image-guided are becoming both more popular and necessary for treating a variety of diseases. To lower the learning curve for new procedures, it is necessary to develop better guidance systems and methods to analyze procedure performance. Since fluoroscopy remains the primary mode of visualizations, the ability to perform catheter tracking from fluoroscopic images is an important part of this endeavor. This paper explores the use of deep learning to perform the landmark detection of a catheter from fluoroscopic images in 3D-printed heart models. We show that a two-stage deep-convolutional-neural-network-based model architecture can provide improved performance by initially locating a region of interest before determining the coordinates of the catheter tip within the image. This model has an average error of less than 2% of the image resolution and can be performed within 4 milliseconds, allowing for its potential use for real-time intraprocedural tracking. Coordinate regression models have the advantage of directly outputting values that can be used for quantitative tracking in future applications and are easier to create ground truth values (~50× faster), as compared to semantic segmentation models that require entire masks to be made. Therefore, we believe this work has better long-term potential to be used for a broader class of cardiac devices, catheters, and guidewires.

LIGHT AT THE END OF TUNNEL- AN INNOVATIVE WAY OF BIOPSY MAPPING

Oral cancer is a crucial global health issue with high morbidity and mortality rate. Oral cancer is preceded by clinically conspicuous oral potentially malignant disorders (OPMDs) and microscopically apparent altered epithelial changes known as oral epithelial dysplasia (OED). The current gold standard in the detection of precancer lesions and conditions is a biopsy. Even an experienced clinician finds it difficult to locate an appropriate biopsy site. To overcome this, a non-invasive, low-cost, portable device known as the Active Biopsy Guidance System (ABGS) has been developed. It is a software that combined multimodal optical imaging and microscopic imaging with real-time projection of cancer risk maps onto the field of view. ABGS, appropriately locate the biopsy site and improve the diagnostic precision. In this review, we attempt to demonstrate a biopsy guidance system, a versatile, quantitative precise tool in biopsy site selection.

Aircrafts Dedicated Guidance System based on improved mathematical model with Modern Visual Presentation

This paper aims to introduce an alternative method of navigating aerial transportation. Its goal is to aid pilot while conducting two most dangerous operations: taking off and landing the plane. Proposed system novelty lies in its separation from all the other systems available on deck. Any fault of hardware or software on the plane will not influence the proposed system. It will offer second set of data for the pilot, precise information about what is visible from the position of the sensor — at the bottom of fuselage. This can greatly increase safety while conducting manoeuvres in bad weather conditions. We also believe that we reached simplicity and cost of implementation of the proposed system at a level good enough to make this idea very popular among industry specialists. From the perspective of mathematical specialist, it offers interesting point of view on methods how algorithm and equation were created.Keywords: aircraft system, algebraical analyse of approach, approach landing system, intelligent aircraft system, visual aircraft system

Efficiency of ferromagnetic shielding of superconducting coils of high-speed maglev crew

Background: Magnetic levitation transport with combined traction, suspension and guidance systems based on superconducting coils (SCC) allows reaching speeds of up to 500600 km/h with a very significant (up to 150200 mm) air gap, which is an important factor in ensuring the safety of high-speed transportation. However, SCC are a source of strong external magnetic fields, which, in conditions of limited crew dimensions, can have a harmful effect on both passengers and on-board auxiliary equipment.
 Aim: to analyze the external magnetic fields of the SCC systems for traction, suspension and direction of maglev vehicles and the effectiveness of ferromagnetic screens as a means of ensuring the electromagnetic safety of passengers and the electromagnetic compatibility of the SCC with other onboard equipment.
 Materials and methods: to achieve this goal, methods of analytical and numerical modeling of the magnetic fields of the SCC using modern software packages were used. As a prototype of the combined magnetic system of traction, suspension and direction, the system of the MLX-L0 maglev vehicle, which is undergoing pilot commercial operation at the Yamanashi test line (Japan), was adopted.
 Results: it is shown that the value of the external magnetic fields of the SCC of the traction and guiding systems in the passenger cabin of the prototype crew exceeds the maximum permissible levels established by domestic and foreign regulatory documents, both without shielding and with passive shielding with flat steel sheets.
 Conclusion: passive shielding of the SCC with steel sheets, including multilayer ones, does not provide the required reduction in the level of external magnetic fields for a given vehicle dimensions.

Safety Aspects of Supporting Apron Controllers with Automatic Speech Recognition and Understanding Integrated into an Advanced Surface Movement Guidance and Control System

The information air traffic controllers (ATCos) communicate via radio telephony is valuable for digital assistants to provide additional safety. Yet, ATCos have to enter this information manually. Assistant-based speech recognition (ABSR) has proven to be a lightweight technology that automatically extracts and successfully feeds the content of ATC communication into digital systems without additional human effort. This article explains how ABSR can be integrated into an advanced surface movement guidance and control system (A-SMGCS). The described validations were performed in the complex apron simulation training environment of Frankfurt Airport with 14 apron controllers in a human-in-the-loop simulation in summer 2022. The integration significantly reduces the workload of controllers and increases safety as well as overall performance. Based on a word error rate of 3.1%, the command recognition rate was 91.8% with a callsign recognition rate of 97.4%. This performance was enabled by the integration of A-SMGCS and ABSR: the command recognition rate improves by more than 15% absolute by considering A-SMGCS data in ABSR.

Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model

Targeted delivery of magnetic nanoparticles (MNPs) to an area of a blood vessel with fluidic flow is hampered by the lack of a suitable real‐time imaging modality for MNPs, the control system complexity, and low targeting performance. Herein, an offline programming guidance (OLPG) scheme for aggregated MNPs is proposed based on a real‐time aggregation volume estimator. The proposed aggregation volume estimator based on a magnetic drug‐targeting simulator reflects volume changes of aggregated MNPs; hence, it can model a magnetic force acting on aggregated MNPs in real time while enhancing targeting performance. The proposed guidance system is evaluated using a simulation testbed and in vitro model of the rat brain, which yields comparable results at different fluid viscosities, flow velocities, target areas, and flow types. The OLPG with the aggregation volume estimator improves targeting performance by 116%–409% compared with the default mode, and by 111%–180% compared to the performance without the aggregation volume estimator. Furthermore, a guidance margin predicts enhanced targeting performance (root‐mean‐square error < 5%) irrespective of the flow environment. The proposed guidance strategy has the potential to overcome the problems caused by the lack of an imaging modality, control‐system complexity, and low targeting performance.

Impact Angle Guidance Using Computationally Enhanced State-Dependent Differential Riccati-Equation Scheme

This study considers the latest three-dimensional impact angle guidance based on the state-dependent differential Riccati-equation (SDDRE) scheme, and it presents novel theories that efficiently guarantee the SDDRE’s applicability and largely reduce the computational burden. The unified applicability analysis completely categorizes the state space in terms of a simple equivalent condition, where all the inapplicable cases (leading to implementation breakdowns) are newly discovered and efficiently resolved. The condition almost removes the tedious online checking routine, which accounts for the dominant effort as endorsed by complexity analysis and practical validations. Moving forward to a general scope, we analyze the computational complexity of such an SDDRE controller: first subject to the MATLAB® framework and then the state-of-the-art enhancements, where the latter come from the best performance among extensive trials. Finally, numerical and hardware experiments (notably, microcontroller and field-programmable gate array) strengthen the confidence in the analytical findings, and they enrich the value in robustness and generality that benefit more guidance or control systems.

Career Guidance System Using Machine Learning

Career Guidance System is important for students going through academic and pursuing courses to access their capabilities and identify their interests. This paper mainly concentrates on career guidance through personality prediction. It helps them decide which job role suits the best based on their performance and other evaluations. The system will predict the Big Five personality, and VAK learning type of the users through data collected from web-based questionnaires. The system recommends different career choices based on their answers. Machine learning algorithms will be used to check the performance of the system and validate the result.

DESIGNING A HOSPITAL SIGNAGE GUIDANCE SYSTEM USING ENVIRONMENTAL DESIGN ELEMENTS

This project analyses hospital signage and navigation systems utilising the collaborative design. It's a new "human-centered" design theory. Community design provides equal, safe, and easy access to the medical environment for all, especially for hospital patients with special needs. In medical facilities, the communal design lets us build passenger flow-directing systems and better signs and guides. Design thinking drives medical signage and counseling. Second, it provides a framework for constructing medical institution signage and orientation systems based on audience needs, signage placement, and environmental design. Applying this design theory and approach to medical institution signs and guiding systems increases audience, sign recognition, patient guidance, treatment flow optimisation, and efficiency. A case study examined the Second People's Hospital of Henan Province's signage and guiding system, patients' physiological and psychological qualities, and their needs and expectations. This project studies and implements signage and guidance system design guidelines. This new initiative aims to encourage researchers and professionals to utilise the common design and present more effective design examples.

Development concept of inter-satellite laser communication

The relevance of this research work is dictated, among other things, by the need to ensure higher requirements for the accuracy characteristics of the navigation field created by spacecraft of the GLONASS navigation system in order to meet the high requirements for the accuracy characteristics of navigation determinations over long intervals of autonomous operation of spacecraft currently put forward by consumers. The presented concept of development uses the latest achievements of laser and radio engineering both to increase the throughput, noise immunity of transmitting information, as well as to reduce the mass and power consumption of onboard equipment, and to conduct inter-satellite range measurements and synchronization of onboard time scales of spacecraft navigation systems, including and for linking the system time scale of space navigation systems (or the group orbital time scale) to the time scale of the State standard of time and frequency. Ensuring the accurate operation of the laser guidance system in space is considered one of the critically important tasks, since its full-scale tests on Earth are almost impossible. Key experimental and also experimental works are analyzed according to the problems of constructing optical links of inter-satellite communication.

Comparison of pulmonary congestion severity using artificial intelligence-assisted scoring versus clinical experts: a secondary analysis of BLUSHED-AHF.

Acute decompensated heart failure (ADHF) is the leading cause of cardiovascular hospitalizations in the United States. Detecting B-lines through lung ultrasound (LUS) can enhance clinicians' prognostic and diagnostic capabilities. Artificial intelligence/machine learning (AI/ML)-based automated guidance systems may allow novice users to apply LUS to clinical care. We investigated whether an AI/ML automated LUS congestion score correlates with expert's interpretations of B-line quantification from an external patient dataset. This was a secondary analysis from the BLUSHED-AHF study which investigated the effect of LUS-guided therapy on patients with ADHF. In BLUSHED-AHF, LUS was performed and B-lines were quantified by ultrasound operators. Two experts then separately quantified the number of B-lines per ultrasound video clip recorded. Here, an AI/ML-based lung congestion score (LCS) was calculated for all LUS clips from BLUSHED-AHF. Spearman correlation was computed between LCS and counts from each of the original three raters. A total of 3858 LUS clips were analysed on 130 patients. The LCS demonstrated good agreement with the two experts' B-line quantification score (r = 0.894, 0.882). Both experts' B-line quantification scores had significantly better agreement with the LCS than they did with the ultrasound operator's score (p < 0.005, p < 0.001). Artificial intelligence/machine learning-based LCS correlated with expert-level B-line quantification. Future studies are needed to determine whether automated tools may assist novice users in LUS interpretation.

Application and Development of Fiber Optic Gyroscope Inertial Navigation System in Underground Space

Fiber Optic Gyroscope Inertial Navigation System (FOG-INS) is a navigation system using fiber optic gyroscopes and accelerometers, which can offer high-precision position, velocity, and attitude information for carriers. FOG-INS is widely used in aerospace, marine ships, and vehicle navigation. In recent years, it has also played an important role in underground space. For example, in the deep earth, FOG-INS can be used in directional well drilling, which can enhance recovery in resource exploitation. While, in shallow earth, FOG-INS is a high-precision positioning technique that can guide construction in trenchless underground pipelaying. This article extensively reviews the application status and latest progress of FOG-INS in underground space from three aspects—FOG inclinometer, FOG drilling tool attitude measurement while drilling (MWD) unit, and FOG pipe-jacking guidance system. First, measurement principles and product technologies are introduced. Second, the research hot spots are summarized. Finally, the key technical issues and future trends for development are put forward. The findings of this work are useful for further research in the field of FOG-INS in underground space, which not only provides new ideas and directions for scientific research, but also offers guidance for subsequent engineering applications.

I-AVR: IoT-Based Ambulatory Vitals Monitoring and Recommender System

In this paper, we propose and implement i-AVR, an Internet of Things (IoT)-based critical-aware system for point-of-care recommendation during ambulatory in-transits. The delay due to ambulances stuck in traffic congestion, disruptive roadways, and far-away hospitals restrain the smooth ambulance services. Therefore, in order to assist the time-critical scenario of a hospital-bound patient, we consider a guidance system to address the necessity. Moreover, these patients require continuous vitals monitoring, which may vary with the progress of time, to reduce the response time upon reaching the destination. The implemented i-AVR comprises two units – a portable healthcare unit and an android navigation unit. The healthcare unit aims to compute the criticality index of the en-route patient and recommend the nearest healthcare center while the navigation unit recommends the convenient route in case of any anomaly in vitals. We show the effectiveness of i-AVR regarding network performance while highlighting the response time of the system. We observe the system response time for computation in orders of seconds and inter-unit communication in milliseconds. Eventually, this analysis indicates the effectiveness of i-AVR in providing quick decisions during the time-critical situations. Our implementation provides essential intervention towards IoT-based healthcare technologies.

Nest-associated scent marks help bumblebees localizing their nest in visually ambiguous situations.

Social insects such as ants and bees are excellent navigators. To manage their daily routines bumblebees, as an example, must learn multiple locations in their environment, like flower patches and their nest. While navigating from one location to another, they mainly rely on vision. Although the environment in which bumblebees live, be it a meadow or a garden, is visually stable overall, it may be prone to changes such as moving shadows or the displacement of an object in the scenery. Therefore, bees might not solely rely on visual cues, but use additional sources of information, forming a multimodal guidance system to ensure their return home to their nest. Here we show that the home-finding behavior of bumblebees, when confronted with a visually ambiguous scenario, is strongly influenced by natural scent marks they deposit at the inconspicuous nest hole when leaving their nest. Bumblebees search for a longer time and target their search with precision at potential nest locations that are visually familiar, if also marked with their natural scent. This finding sheds light on the crucial role of odor in helping bees find their way back to their inconspicuous nest.

Leadership Caderization Development at Pesantren Darunnajah Jakarta

This research was motivated by the writer's anxiety about the weaknesses of pesantren institutions in educating successive leaders because pesantren rarely survived more than a century. As a form of effort toward realizing ideal leadership capacity development, the writer chose Pesantren Darunnajah Jakarta as a subject of research. This type of research is qualitative according to the Case Study Research Theory. Based on the results, the researcher found that the concept of leadership cadre is delegation, transformation based on the values of Islamic teachings that involve all pesantren staff in the educational process from the pesantren leadership, and implementation of leadership cadre development at the pesantren Darunnajah Jakarta, which is contained in the guidance and coaching system, namely the Total Quality Management system, for 24 hours so that prospective cadres of leaders always receive supervision, guidance, and coaching.

Needs, Roles, and Challenges of Young Asian Neurosurgeons

Asia has a marked shortage of neurosurgical care, with approximately 2.5 million critical cases left untreated. The Young Neurosurgeons Forum of the World Federation of Neurosurgical Societies surveyed Asian neurosurgeons to identify research, education, and practice. A cross-sectional study using a pilot-tested e-survey was circulated to the Asian neurosurgical community from April to November 2018. Descriptive statistics were used to summarize variables pertaining to demographics and neurosurgical practices. The chi-square test was used to explore the relationship between World Bank income level and variables on neurosurgical practices. A total of 242 responses were analyzed. Respondents were mostly from the low- and middle-income countries (70%). Most represented institutions were teaching hospitals (53%). More than 50% of the hospitals had between 25and 50 neurosurgical beds. Access to an operating microscope (P= 0.038) or image guidance system (P= 0.001) appeared to increase in correlation to a higher World Bank income level. Limited opportunities for conducting research (56%) and hands-on operating opportunities (45%) were leading challenges in daily academic practice. The leading challenges were limited numbers of intensive care unit beds (51%), inadequate or absent insurance coverage (45%), and lack of organized perihospital care (43%). Inadequate insurance coverage decreased with increasing World Bank income levels (P < 0.001). Organized perihospital care (P= 0.001), regular magnetic resonance imaging access (P= 0.032), and equipment necessary for microsurgery (P= 0.007) increased with higher World Bank income levels. Improving neurosurgical care hinges on regional and international collaboration and national policies to ensure universal access to essential neurosurgical care.

Formation Flying Orbit and Control Concept for Virtual Super Optics Reconfigurable Swarm Mission

The Virtual Super Optics Reconfigurable Swarm (VISORS) mission is a distributed telescope consisting of two 6U CubeSats separated by 40 m that will obtain high-resolution images of active solar regions in the extreme ultraviolet spectrum. This mission is challenging because the CubeSats must autonomously control their relative motion with unprecedented accuracy while operating in close proximity. This paper presents three contributions that enable the VISORS mission to meet its challenging requirements. First, passively safe absolute and relative orbit designs for distributed telescopes that provide regular periods of alignment with inertial targets are developed using relative eccentricity/inclination vector separation. Second, a guidance, navigation, and control system design is proposed to meet the demanding relative motion control requirements. Third, a concept of operations is proposed that minimizes mission operations load when the formation is not actively performing observations. This concept of operations includes a safety plan to address on-orbit anomalies. The performance of the guidance, navigation, and control system is validated through Monte Carlo simulations including all significant error sources and operational constraints. These simulations show that the mission requirements are met with margin, providing a preliminary demonstration of the feasibility of accurate autonomous formation control with CubeSats.

Operative Technique and Nuances for the Stereoelectroencephalographic (SEEG) Methodology Utilizing a Robotic Stereotactic Guidance System.

The SEEG methodology has gained favor in North America over the last decade as a means of localizing the epileptogenic zone (EZ) prior to epilepsy surgery. Recently, the application of a robotic stereotactic guidance system for implantation of SEEG electrodes has become more popular in many epilepsy centers. The technique for the use of the robot requires extreme precision in the pre-surgical planning phase and then the technique is streamlined during the operative portion of the methodology, as the robot and surgeon work in concert to implant the electrodes. Herein is detailed precise operative methodology of using the robot to guide implantation of SEEG electrodes. A major limitation of the procedure, namely its heavy reliance on the ability to register the patient to a preoperative volumetric magnetic resonance image (MRI), is also discussed. Overall, this procedure has been shown to have a low morbidity rate and an extremely low mortality rate. The use of a robotic stereotactic guidance system for the implantation of SEEG electrodes is an efficient, fast, safe, and accurate alternative to conventional manual implantation strategies.

Distributed fuzzy learning sliding mode cooperative control for non-affine nonlinear multi-missile guidance systems via a prescribed performance observer

This paper focuses on the distributed fuzzy learning sliding mode cooperative control issue for non-affine nonlinear multi-missile guidance systems. The dynamics of each follower is non-affine form with unknown lumped factor. To estimate the unknown lumped factor, a generalized fuzzy hyperbolic model (GFHM) based prescribed performance observer (PPO) is proposed. Different from the traditional disturbance observers, a residual set of error transient behavior is incorporated additionally so that the peak phenomenon can be avoided. Meanwhile, an auxiliary system is employed to convert the system of each follower to augmented affine form. Then, a distributed fuzzy learning sliding mode cooperative control approach is designed which consists of two parts. The adaptive sliding mode control (SMC) part is designed to force the states to move along the predefined integral sliding surface. For the equivalent sliding dynamics, the distributed optimal control part with GFHM is developed to minimize the cooperative performance function. Thus, the stability and the optimality of the closed-loop system are guaranteed synchronously. Finally, all signals of closed-loop system are rigorously proved bounded and the multi-missile cooperative guidance scenario is applied to verify the effectiveness of proposed method.