Search Results (13)

Single-frequency global navigation satellite system/inertial navigation system (GNSS/INS) integration has wide application prospects in urban environments; however, correct integer ambiguity is the major challenge because of GNSS-blocked environments. In this paper, a sequential strategy of partial ambiguity resolution (PAR) of GNSS/INS for tightly coupled integration based on the robust posteriori residual, elevation angle, and azimuth in the body frame using INS aids is presented. First, the satellite is eliminated if the maximum absolute value of the robust posteriori residuals exceeds the set threshold. Otherwise, the satellites with a minimum elevation angle of less than or equal to 35° are successively eliminated. If satellites have elevation angles greater than 35°, these satellites are divided into different quadrants based on their azimuths calculated in body frame. The satellite with the maximum azimuth in each quadrant is selected as the candidate satellite, the candidate satellites are eliminated one by one, and the remaining satellites are used to calculate the position dilution of the precision (PDOP). Finally, the candidate satellite with the lowest PDOP is eliminated. Two sets of vehicle-borne data with a low-cost GNSS/INS integrated system are used to analyze the performance of the proposed algorithm. These experiments demonstrate that the proposed algorithm has the highest ambiguity fixing rates among all the designed PAR methods, and the fixing rates for these two sets of data are 99.40% and 98.74%, respectively. Additionally, among all the methods compared in this paper, the proposed algorithm demonstrates the best positioning performance in GNSS-blocked environments. Full article

This paper presents a novel circular-ESG framework integrating circular economy (CE) principles with environmental, social, and governance (ESG) criteria to address the lack of uniform sustainability measures. We introduce normalized sustainability coordinates (NSCs) as a comprehensive metric for sustainability performance, reconciling economic development with environmental balance. The circular-ESG model employs a four-quadrant Cartesian system to map business model impacts on natural and socio-economic systems, ranging from linear open-loop to circular closed-loop ESG models. This framework enables empirical analysis through data-driven NSCs (−1 to 1) and establishes temporal key performance indicators. By incorporating the Human Development Index within ecological limits, the model promotes regenerative development aligned with planetary boundaries. The circular-ESG approach offers a practical tool for businesses, households, organizations, and policymakers to navigate sustainable development complexities. This integrated framework fosters innovation and supports a just transition towards regenerative practices, providing a roadmap for high human development within ecological limits. The circular-ESG model advances sustainability science and management, contributing to the discourse on measuring and implementing sustainable practices across sectors and scales. The model is currently conceptual; we encourage empirical validation and further research to explore its practical applications and effectiveness in real-world scenarios. While the provided examples of use cases serve as conceptual demonstrations, future research could empirically apply the model to real-world data. Full article

This study examined the influence of genetic background on cognitive performance in a selectively bred high nicotine-preferring (NP) rat line. Using the novel object recognition (NOR), novel location recognition (NLR), and Morris water maze (MWM) tests, we evaluated object memory, spatial memory, and spatial navigation in nicotine-naive NP rats compared to controls. Our results demonstrate that in the NOR test, both male and female NP rats spent more time exploring the novel object (higher discrimination index) compared to sex-matched controls. In the NLR, the discrimination index differed significantly from zero chance (no preference) in both NP males and females but not in controls, indicating enhanced spatial memory in the NP line. During MWM acquisition, the NP groups and control males took a shorter path to reach the platform compared to control females. On the probe trial, the distance traveled in the target quadrant was longer for NP males and females compared to their respective controls, suggesting enhanced spatial navigation and learning in the NP rats. The interesting preference for novel objects and locations displayed by NP rats may indicate a potential novelty-seeking phenotype in this line. These results highlight the complex interplay between genetic factors, cognitive function, and nicotine preference. Full article

In the last decade, the use of virtual reality (VR) technologies has been increasing, as head-mounted display devices, such as the Meta Quest series, have become commercially accessible to everyday consumers. There are plenty of software applications developed for VR, ranging from games to serious training. Despite the fast-growing emergence of novel and natural gesture-based interface components, traditional windows, icons, menus, and pointer user interfaces still remain popular in these implementations. Therefore, navigating and interacting within 3-dimensional environments can still be challenging in virtual reality. This body of work will serve as a reference to the literature survey on 3-dimensional user interfaces and their implementations, and categorize them to reiterate a set of design recommendations for implementing 3-dimensional user interfaces in virtual reality. While questions revolve around the need for a 3-dimensional user interface if a 2-dimensional user interface suffices, we cannot ignore the capabilities and benefits offered when utilising 3-dimensional space. Unlike other work, this work proposes a novel metaphor-guided quadrant model to delineate the 3-dimensional user interface problem in an end-to-end manner. To achieve this, over 30 unique articles selected using a meta-analyses methodology were reviewed. The result highlights the dimensional semantics of the inputs and dimensional outcomes of the virtual targets. Notable reiterated design recommendations would match devices to interaction and then to the tasks in 3-dimensional environments, reducing the degrees of freedom, imposing constraints when possible, and not implementing one single best interaction technique. The results of this survey will be beneficial for virtual reality simulation designers or researchers attempting to determine the appropriate model when developing user interfaces for virtual reality content. Full article

Patients with amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, an incurable disease in which motor neurons are selectively destroyed, gradually lose their mobility as organ dysfunction begins, and eventually, patients find it challenging to make even minor movements and simple communications. To communicate with patients with quadriplegia, researchers have focused on movements of the eye, the only moving organ for patients with ALS, and they have investigated the detection of eyeblinks using brainwaves or cameras or other ways to select letters on a screen via eyeball movements based on eye-tracking cameras. However, brainwave-based techniques, which use the electrical signals of eye movements to determine patient’s intentions, are sensitive to noise, often resulting in the inaccurate identification of intent. Alternatively, a camera-based method that uses letter selection detects the movement of eye feature-points, and this method makes it easy to identify a patient’s intentions using a predefined decision-making process. However, it has long processing time and is prone to inaccuracy due to errors in either the Morse code implementation assigned to all alphabets or the sequential selection methods. Therefore, we have proposed iMouse-sMc, a simplified Morse code-based user interface model using an eye mouse for faster and easier communication with such patients. Furthermore, we improved the detection performance of the eye mouse by applying image contrast techniques to enable communication with patients even at night. To verify the excellent performance of the proposed eye mouse for a user interface, we conducted comparative experiments with existing camera-based communication models based on various words. The results revealed that the time of communication was reduced to 83 s and the intention recognition accuracy was improved by ~28.16%. Additionally, even in low-light environments, where existing models are unable to communicate with the patients due to difficulties with eye detection, the proposed model demonstrated its eye detection capability and proved that it can be used universally for communication with patients during the day and at night. Full article

The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this context, eco-approach and departure methods have been proposed in the literature. Integrating these methods with regenerative braking allows for safe, power-efficient navigation through intersections and crossroad layouts. This paper proposes rule- and fuzzy inference system-based strategies for a coupled eco-approach and departure regenerative braking system. This analysis is carried out through a numerical simulator based on a three-degree-of-freedom connected electric vehicle model. The powertrain is represented by a realistic power loss map in motoring and regenerative quadrants. The simulations aim to compare both longitudinal navigation strategies by means of relevant metrics: power, efficiency, comfort, and usage duty cycle in motor and generator modes. Numerical results show that the vehicle is able to yield safe navigation while focusing on energy regeneration through different navigation conditions. Full article

Digital health research is an emerging discipline that requires easy-to-understand theoretical frameworks and implementation models for digital health providers in health and social care settings. The COVID-19 pandemic has heightened the demand for digital health discipline-specific instruction on how to manage evidence-based digital health transformation. Access to the use of these models guarantees that digital health providers can investigate phenomena using safe and suitable approaches and methods to conduct research and identify answers to challenges and problems that arise in health and social care settings. The COMPASS theory is designed to aid transformation of health and social care environments. A navigational rose of primary quadrants is divided by four main compass points, with person-centred care being central to the philosophy. Two axes produce Cartesian planes that intersect to form a box plot, which can be used to discover human and physical resource weightings to augment digital health research design and implementation. A third continuum highlights stakeholders’ capabilities, which are critical for any multidisciplinary study. The COMPASS mnemonic guides end users through the process of design, development, implementation, evaluation, and communication of digital health transformations. The theory’s foundations are presented and explained in context of the ‘new normal’ of health and social care delivery. Full article

Global and regional positional accuracy assessment is of the highest importance for any satellite navigation system, including the Global Positioning System (GPS). Although positioning error can be expressed as a vector quantity with direction and magnitude, most of the research focuses on error magnitude only. The positional accuracy can be evaluated in terms of navigational quadrants as further refinement of error distribution, as it was shown here. This research was conducted in the wider area of the Northern Adriatic Region, employing the International Global Navigation Satellite Systems (GNSS) Service (IGS) data and products. Similarities of positional accuracy and deviations distributions for Single Point Positioning (SPP) were addressed in terms of magnitudes. Data were analyzed during the 11-day period. Linear and circular statistical methods were used to quantify regional positional accuracy and error behavior. This was conducted in terms of both scalar and vector values, with assessment of the underlying probability distributions. Navigational quadrantal positioning error subset analysis was carried out. Similarity in the positional accuracy and positioning deviations behavior, with uneven positional distribution between quadrants, indicated the directionality of the total positioning error. The underlying distributions for latitude and longitude deviations followed approximately normal distributions, while the radius was approximated by the Rayleigh distribution. The Weibull and gamma distributions were considered, as well. Possible causes of the analyzed positioning deviations were not investigated, but the ultimate positioning products were obtained as in standard, single-frequency positioning scenarios. Full article

The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems’ development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment. Full article

Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interacting with the working environment. To overcome this problem, in this work, an agent is computationally devised that mimics the grid and place neuron functionality to learn cognitive maps from the input spatial data of an environment or an object. A novel quadrant-based approach is proposed to model the behavior of the grid neuron, which, like the real grid neuron, is capable of generating periodic hexagonal grid-like output patterns from the input body movement. Furthermore, a cognitive map formation and their learning mechanism are proposed using the place–grid neuron interaction system, which is meant for making predictions of environmental sensations from the body movement. A place sequence learning system is also introduced, which is like an episodic memory of a trip that is forgettable based on their usage frequency and helps in reducing the accumulation of error during a visit to distant places. The model has been deployed and validated in two different spatial data learning applications, one being the 2D object detection by touch, and another is the navigation in an environment. The result analysis shows that the proposed model is significantly associated with the expected outcomes. Full article

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration—which are the basis of tracking error estimation—are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (−0.25 cycle, 0.25 cycle) to (−0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz $\cup$ (26, 50) dB-Hz. Full article

The thermal environment is an important factor in the design of optical systems. This study investigated the thermal analysis technology of optical systems for navigation guidance and control in supersonic aircraft by developing empirical equations for the front temperature gradient and rear thermal diffusion distance, and for basic factors such as flying parameters and the structure of the optical system. Finite element analysis (FEA) was used to study the relationship between flying and front dome parameters and the system temperature field. Systematic deduction was then conducted based on the effects of the temperature field on the physical geometry and ray tracing performance of the front dome and rear optical lenses, by deriving the relational expressions between the system temperature field and the spot size and positioning precision of the rear optical lens. The optical systems used for navigation guidance and control in supersonic aircraft when the flight speed is in the range of 1–5 Ma were analysed using the derived equations. Using this new method it was possible to control the precision within 10% when considering the light spot received by the four-quadrant detector, and computation time was reduced compared with the traditional method of separately analysing the temperature field of the front dome and rear optical lens using FEA. Thus, the method can effectively increase the efficiency of parameter analysis and computation in an airborne optical system, facilitating the systematic, effective and integrated thermal analysis of airborne optical systems for navigation guidance and control. Full article

COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS’s accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load. Full article