Autonomic System Research Articles

FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.

Leveraging artificial intelligence for enhanced supply chain optimization

This study provides a comprehensive review of the integration of Artificial Intelligence (AI) into Supply Chain Management (SCM), focusing on its impact on operational efficiency, strategic innovation, and sustainability. Employing a systematic literature review and content analysis methodology, the research synthesizes findings from peer-reviewed articles and conference papers published between 2013 and 2023. The study identifies key advancements in AI technologies, such as machine learning, natural language processing, and robotics, and their applications across various supply chain processes including demand forecasting, inventory management, and logistics optimization. Key findings reveal that AI significantly enhances supply chain efficiency by improving decision-making, reducing costs, and optimizing resource allocation. However, challenges such as data privacy concerns, ethical considerations, and the need for skilled personnel emerge as critical factors influencing AI adoption in SCM. The future outlook for AI-enhanced supply chains is promising, with potential for further innovation and resilience, albeit contingent upon addressing existing challenges. The study concludes with strategic recommendations for practitioners and policymakers, emphasizing the importance of fostering a culture of innovation, developing digital competencies, and creating supportive regulatory frameworks for AI integration. Directions for future research include exploring the long-term impacts of AI on supply chain sustainability, ethical implications of autonomous systems, and the interplay between AI and emerging technologies. This research contributes to the academic discourse on AI in SCM, offering insights for enhancing supply chain operations in the digital age.

Nonlinear analysis of the synchronous reference frame phase-locked loop under unbalanced grid voltage

AbstractThe synchronous reference frame phase-locked loop (SRF-PLL), also called dqPLL, is an electric circuit commonly used in power electronics to estimate the phase angle of a three-phase AC grid. If the voltage is unbalanced, the PLL is modeled as a periodically forced nonlinear oscillator and is known in practice to converge to a steady oscillation. In the existing literature, the oscillation has been studied via linearization assuming a low level of unbalance. Aiming for stronger nonlocal statements, we present nonlinear analysis. We apply the method of autonomous comparison systems and incremental stability to show that the steady oscillation is unique and attractive in a wider neighborhood. Its lock-in domain is estimated using numerical phase portrait analysis. The oscillation is further approximated up to the terms of the second order in the unbalance factor—it yields an estimation of the time average of the PLL’s phase error which is not visible by linearization only. The results provide stability guarantees and can guide the tuning of SRF-PLL.

Development of an Automatic Baby Cradle System

Infant care can be challenging in the fast-paced atmosphere of today, where parental responsibilities can compete with rigorous employment. An important step toward improving newborn health and reducing parent concern is the incorporation of an autonomous baby cradle system. This cutting-edge framework combines thorough planning with the seamless incorporation of several childcare components, providing carers with a priceless tool. Modern technology is incorporated into the system, including an Espressif Systems32 camera that allows parents to monitor children from a distance, a voice recognition system that enables swinging to react to spoken cues, and an advanced moisture sensor that monitors changes in bedding conditions. A sensor boosts the system’s effectiveness and adds to its excellent mobility and versatility. In a hectic setting, the Automatic Baby Cradle System redefines infant care. This system offers a comprehensive solution that addresses several aspects of newborn well-being by effortlessly integrating technology into caring. Modern capabilities like speech recognition, temperature management, wetness sensing, and remote monitoring show how technology has the power to fundamentally alter parenting habits. This research lays the framework for future developments and advances in newborn care and parental assistance as technology advances

High-recall calibration monitoring for stereo cameras

Cameras are the prevalent sensors used for perception in autonomous robotic systems, but their initial calibration may degrade over time due to dynamic factors. This may lead to a failure of downstream tasks, such as simultaneous localization and mapping (SLAM) or object recognition. Hence, a computationally lightweight process that detects the decalibration is of interest. We describe a modification of StOCaMo, an online calibration monitoring procedure for a stereoscopic system. The method uses robust kernel correlation based on epipolar constraints; it validates extrinsic calibration parameters on a single frame with no temporal tracking. In this paper, we present a modified StOCaMo with an improved recall rate on small decalibrations through a confirmation technique based on resampled variance. With fixed parameters learned on a realistic synthetic dataset from CARLA, StOCaMo and its proposed modification were tested on multiple sequences from two real-world datasets: KITTI and EuRoC MAV. The modification improved the recall of StOCaMo by 25 % (to 91 % and 82 %, respectively), and the accuracy by 12 % (to 94.7 % and 87.5 %, respectively), while labeling at most one-third of the input data as uninformative. The upgraded method achieved the rank correlation between StOCaMo V-index and downstream SLAM error of 0.78 (Spearman).

Adverse Effects of Meditation: Autonomic Nervous System Activation and Individual Nauseous Responses During Samadhi Meditation in the Czech Republic.

Buddhist meditation practices, including Samadhi meditation, which forms the basis for mindfulness practice, are broadly promoted as pathways to wellbeing, but evidence of their adverse effects is emerging. In a single-group observational study with assessments of autonomic system before, during, and after Samadhi meditation, we explore the relationship between post-meditation nausea symptoms and the degree of change in autonomic system activity during meditation as compared to before and after in 57 university students (42 women; mean age = 22.6) without any previous experience in meditation or yoga practices. We hypothesize that nauseous feelings in meditation are connected to a rapid increase of activity in the sympathetic nervous system, as indicated by decreased heart-rate variability (HRV). We additionally explore links between meditation-induced nausea and two markers of parasympathetic activity: increased HRV and vasovagal syncope. Engaging in meditation and increased nausea during meditation were both associated with increased markers of HRV parasympathetic activity, but 12 individuals with markedly higher nausea demonstrated increased HRV markers of sympathetic activity during meditation. Vasovagal syncope was observed but found to be unrelated to nausea levels. Drivers of adverse effects of meditation in some individuals require further investigation.

Intelligent railroad inspection and monitoring

Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications.

Controlling the dynamic behavior of decentralized cluster through centralized approaches

Abstract How to control the dynamic behavior of large-scale artificial active matter is a critical concern in experimental research on soft matter, particularly regarding the emergence of collective behaviors and the formation of group patterns. Centralized systems excel in precise control over individual behavior within a group, ensuring high accuracy and controllability in task execution. Nevertheless, their sensitivity to group size may limit adaptability to diverse tasks. In contrast, decentralized systems empower individuals with autonomous decision-making, enhancing adaptability and system robustness. Yet, this flexibility comes at the cost of reduced accuracy and efficiency in task execution. In this work, we present a unique method for regulating the centralized dynamic behavior of self-organizing clusters based on environmental interactions. Within this environment-coupled robot system, each robot possesses similar dynamic characteristics, and their internal programs are entirely identical. However, their behaviors can be guided by the centralized control of the environment, facilitating the accomplishment of diverse cluster tasks. This approach aims to balance the accuracy and flexibility of centralized control with the robustness and task adaptability of decentralized control. The proactive regulation of dynamic behavioral characteristics in active matter groups, demonstrated in this work through environmental interactions, holds the potential to introduce a novel technological approach and provide experimental references for studying the dynamic behavior control of large-scale artificial active matter systems.

Dynamical system analysis of scalar field cosmology in coincident f(Q) gravity

In this article, we investigate scalar field cosmology in the coincident f(Q) gravity formalism. We calculate the motion equations of f(Q) gravity under the flat Friedmann-Lemaître-Robertson-Walker background in the presence of a scalar field. We consider a non-linear f(Q) model, particularly f(Q) = − Q + α Q n , which is nothing but a polynomial correction to the Symmetric Teleparallel Equivalent to General Relativity (STEGR) case. Further, we assumed two well-known specific forms of the potential function, specifically the exponential from V(ϕ) = V 0 e −β ϕ and the power-law form V(ϕ) = V 0 ϕ −k . We employ some phase-space variables and transform the cosmological field equations into an autonomous system. We calculate the critical points of the corresponding autonomous systems and examine their stability behaviors. We discuss the physical significance corresponding to the exponential case for parameter values n = 2 and n = − 1 with β = 1, and n = − 1 with β=3 . Moreover, we discuss the same corresponding to the power-law case for the parameter value n = − 2 and k = 0.16. We also analyze the behavior of corresponding cosmological parameters such as scalar field and dark energy density, deceleration, and the effective equation of state parameter. Corresponding to the exponential case, we find that the results obtained for the parameter constraints in Case III is better among all three cases, and that represents the evolution of the Universe from a decelerated stiff era to an accelerated de-Sitter era via matter-dominated epoch. Further, in the power-law case, we find that all trajectories exhibit identical behavior, representing the evolution of the Universe from a decelerated stiff era to an accelerated de-Sitter era. Lastly, we conclude that the exponential case shows better evolution as compared to the power-law case.

Mapping early brain-body interactions: associations of fetal heart rate variation with newborn brainstem, hypothalamic, and dorsal anterior cingulate cortex functional connectivity.

The autonomic nervous system (ANS) regulates the body's physiology, including cardiovascular function. As the ANS develops during the second to third trimester, fetal heart rate variability (HRV) increases while fetal heart rate (HR) decreases. In this way, fetal HR and HRV provide an index of fetal autonomic nervous system development and future neurobehavioral regulation. Fetal HR and HRV have been associated with child language ability and psychomotor development behavior in toddlerhood. However, their associations with post-birth autonomic brain systems, such as the brainstem, hypothalamus, and dorsal anterior cingulate cortex (dACC), have yet to be investigated even though brain pathways involved in autonomic regulation are well established in older individuals. We assessed whether fetal HR and HRV were associated with the brainstem, hypothalamic and dACC functional connectivity in newborns. Data were obtained from 60 pregnant individuals (ages 14-42) at 24-27 and 34-37 weeks gestation using a fetal actocardiograph to generate fetal HR and HRV. During natural sleep, their infants (38 males and 22 females) underwent a fMRI scan between 40-46 weeks of postmenstrual age. Our findings relate fetal heart indices to brainstem, hypothalamic, and dACC connectivity and reveal connections with widespread brain regions that may support behavioral and emotional regulation. We demonstrated the basic physiologic association between fetal HR indices and lower and higher order brain regions involved in regulatory processes. This work provides the foundation for future behavioral or physiological regulation research in fetuses and infants.Significance statement Fetal heart rate indices are quantifiable, developmental markers of the fetal autonomic nervous system. Variations in their trajectories can signal compromised neurodevelopmental outcomes. We assessed associations between fetal heart rate indices and early infant brain development to identify unique or common associations corresponding to autonomic nervous system maturation patterns. We found associations between fetal heart rate indices and infant brainstem, hypothalamic, and dACC connectivity-areas that support autonomic and behavioral regulatory functions. The study demonstrates that these associations between ANS and brain regions involved in autonomic regulation exist early in life. These findings are a first step to understanding how these brain connections form the basis of future regulatory development.

An open-loop control algorithm for improved tracking in a heliostat

The growing energy demand and its relation to climate change have driven the search for sustainable alternatives, such as concentrated solar energy. In this context, heliostats play a crucial role by reflecting and concentrating solar light onto a receiver. However, traditional control approaches based on geographical data have limitations. This study introduces an autonomous control system for heliostats that eliminates the need for preloaded geographical data. The approach is based on communication between the heliostat and the solar tracker, with two configuration modes: map calibration and automatic. Centralized and autonomous heliostats are distinguished, with the latter being the focus of the study. Autonomous heliostats have their own control system and can make decisions regarding positioning and safety. The methodology involves a mathematical algorithm that calculates the optimal rotation and tilt of the heliostat to redirect light toward a target. Simulation and physical prototype testing validate a remarkable consistency between simulated and experimental data. A key result is the surprising similarity of 97.9% between the obtained data, validating the algorithm's effectiveness. This study provides a robust approach for designing autonomous heliostat control systems, integrating simulation and experimentation. These results support the algorithm's precision and ability to direct solar radiation effectively. Expanding towards autonomous control and complete heliostat system evaluation facilitates the path toward more efficient and sustainable concentrated solar energy.

Bioinspiration from bats and new paradigms for autonomy in natural environments

Achieving autonomous operation in complex natural environment remains an unsolved challenge. Conventional engineering approaches to this problem have focused on collecting large amounts of sensory data that are used to create detailed digital models of the environment. However, this only postpones solving the challenge of identifying the relevant sensory information and linking it to action control to the domain of the digital world model. Furthermore, it imposes high demands in terms of computing power and introduces large processing latencies that hamper autonomous real-time performance. Certain species of bats that are able to navigate and hunt their prey in dense vegetation could be a biological model system for an alternative approach to addressing the fundamental issues associated with autonomy in complex natural environments. Bats navigating in dense vegetation rely on clutter echoes, i.e. signals that consist of unresolved contributions from many scatters. Yet, the animals are able to extract the relevant information from these input signals with brains that are often less than 1 g in mass. Pilot results indicate that information relevant to location identification and passageway finding can be directly obtained from clutter echoes, opening up the possibility that the bats’ skill can be replicated in man-made autonomous systems.

ROV-Based Autonomous Maneuvering for Ship Hull Inspection with Coverage Monitoring

Hull inspection is an important task to ensure sustainability of ships. To overcome the challenges of hull structure inspection in an underwater environment in an efficient way, an autonomous system for hull inspection has to be developed. In this paper, a new approach to underwater ship hull inspection is proposed. It aims at developing the basis for an end-to-end autonomous solution. The real-time aspect is an important part of this work, as it allows the operators and inspectors to receive feedback about the inspection as it happens. A reference mission plan is generated and adapted online based on the inspection findings. This is done through the processing of a multibeam forward looking sonar to estimate the pose of the hull relative to the drone. An inspection map is incrementally built in a novel way, incorporating uncertainty estimates to better represent the inspection state, quality, and observation confidence. The proposed methods are experimentally tested in real-time on real ships and demonstrate the applicability to quickly understand what has been done during the inspection.

THE IMPACT OF AI ON EMPLOYMENT AND ORGANISATION IN THE INDUSTRIAL WORKING ENVIRONMENT OF THE FUTURE

In manufacturing companies, artificial intelligence (AI) applications like robotics, automation, and intelligent support are driving a broad transformation process that impacts not only the usage of algorithms but also people and organization. The working world will undergo a long-lasting transformation due to automation and algorithmization, whereby every value-adding activities will be impacted, ranging from skilled labour and management to operational manufacturing operations. AI's capacity for learning is predicted to allow it to act independently, aid people through systems, use resources more wisely, improve the environmental impact of operations, and open up new working models with direct involvement and increased transparency. It ought to boost productivity, improve client happiness, and simplify and enliven work. According to recent studies, the success of digitalization depends less on technology and investment and more on leaders' and employees can do when it comes to creating a supportive organizational culture and structure.The AI's impact on jobs is debatable. It ought to result in challenging and safe work, respite from physical and mental strain, and an improvement in work-life balance. However, there are worries about loss of jobs, disqualification, increasing digital system autonomy, and greater employee authority. But data shows that historically, two humans in the business have been replaced by one robot on average, while two new employments have been generated outside. AI will most likely act in a like manner. AI deployment necessitates management reorganization, teamwork, codetermination, qualification, and extensive knowledge sharing. The involvement Future leadership behaves flexibly within the parameters of democratically established interdisciplinary teams and self-organizing networks. Executives consider themselves to be moderators and coaches. Based on an extensive assessment of the literature, this research investigates the effects of AI's introduction in industrial businesses. Effects on employment, organizational culture, and structure will receive special consideration. We'll also look at industrial company AI applications that represent best practices. Finally, a critical conversation looks at the tools and potential for using AI to shape internal company transformation with all the necessary parties involved.

Criminal Liability for Artificial Intelligence and Autonomous Systems

The emergence of artificial intelligence and the increasing dependence of organisations on these modes of modern technology make this subject quite an intriguing area of research. The research has been conducted in Saudi Arabia, where the emphasis was laid on the criminal liability affiliated with the various firms operating through artificial intelligence and its modern use. How the different liabilities are held upon the organisations and what measures are taken in order to address these issues were all discussed. The role played by the legal authorities was discussed in terms of how these divide the accountabilities of the multiple parties involved.

Adopting sea water reverse osmosis desalination technologies for sustainable development: lessons learnt from Algiers Science and Technology for membranes 2023 workshop

The city of Algiers hosted the workshop on Science and Technology for Membranes from 30th May to 1st June 2023—STM 2023, held in Algeria. The main topics focused on Reverse Osmosis (RO) desalination technologies, decentralized autonomous systems, wastewater recycling for circularity, and the effects of green membrane processes on socio-economic growth. This review examines the influence of global trends and instabilities (i.e., climate change, disrupted supply chain platforms, natural disasters) on the steady supply of freshwater. The implementation of RO desalination solutions and their sustainability are assessed based on resiliency factors, which is helpful to screen water treatment alternatives based on technological benefits, ecological safety and environmental protection. Key takeaways from this study call for rolling out local membrane production capabilities, promoting the benefits of autonomous decentralized treatment systems, prioritizing innovative solutions to reduce greenhouse gas emissions, and for developing RO-specific equipment recycling infrastructures. Implementing these new tactics can lead to mitigate water scarcity and enable a sustainable deployment of desalination membrane technologies in Algeria and its region.

Simplified matching pursuits applied to 3D nuclear reactor temperature distribution construction

Autonomous control systems can enhance the economic viability of complex systems such as nuclear microreactors. Advanced forms of these control systems benefit from techniques for high-resolution field data reconstruction from sensor data. Basis projection methods present a useful set of methodologies for nonparametric reconstruction of these field data. In this work, high fidelity multiphysics simulations are used to generate microreactor temperature distributions that are then used to evaluate the viability of a basis projection method which employs a generalized set of basis functions, or dictionaries. Additionally, metrics for evaluating the accuracy of the temperature distribution are presented that are motivated both from the fields of traditional signal processing and statistics. The performance of a method based on matching pursuits is compared to a method that uses the sequential ordering of basis functions. The matching pursuits-based method gives improved performance for highly sparse representations of temperature distributions. Additionally, accuracy is analyzed for multiple temperature distributions which represent different potential reactor operation configurations. Overall, the results from this work should be considered as an upper bound on the performance of basis projection methods which employ generalized basis functions for real time reactor control.

Collaborative Misbehaviour Response System for Improving Road Safety

This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system’s limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms.

Spreading speeds in an asymptotic autonomous system with application to an epidemic model

This article studies the initial value problem in an asymptotic autonomous reaction–diffusion system. Namely, when the time goes to infinity, these parameters converge to constants. The system may be nonmonotonic. With different decaying initial conditions, the leftward and rightward spreading speeds are estimated by constructing auxiliary functions and systems. As an application, we present the propagation properties in a diffusive epidemic model with different decaying initial values, which does not satisfy the classical comparison principle of mixed quasimonotone systems.

The Impact of Prenatal Alcohol Exposure on the Autonomic Nervous System and Cardiovascular System in Rats in a Sex-Specific Manner

Background: Fetal Alcohol Spectrum Disorder (FASD) is a consequence of prenatal alcohol exposure (PAE) associated with a range of effects, including dysmorphic features, prenatal and/or postnatal growth problems, and neurodevelopmental difficulties. Despite advances in treatment methods, there are still gaps in knowledge that highlight the need for further research. The study investigates the effect of PAE on the autonomic system, including sex differences that may aid in early FASD diagnosis, which is essential for effective interventions. Methods: During gestational days 5 to 20, five pregnant female Wistar rats were orally administered either glucose or ethanol. After 22 days, 26 offspring were born and kept with their mothers for 21 days before being isolated. Electrocardiographic recordings were taken on the 29th and 64th day. Heart rate variability (HRV) parameters were collected, including heart rate (HR), standard deviation (SD), standard deviation of normal-to-normal intervals (SDNN), and the root mean square of successive differences between normal heartbeats (RMSSD). Additionally, a biochemical analysis of basic serum parameters was performed on day 68 of the study. Results: The study found that PAE had a significant impact on HRV. While electrolyte homeostasis remained mostly unaffected, sex differences were observed across various parameters in both control and PAE groups, highlighting the sex-specific effects of PAE. Specifically, the PAE group had lower mean heart rates, particularly among females, and higher SDNN and RMSSD values. Additionally, there was a shift towards parasympathetic activity and a reduction in heart rate entropy in the PAE group. Biochemical changes induced by PAE were also observed, including elevated levels of alanine transaminase (ALT) and aspartate aminotransferase (AST), especially in males, increased creatinine concentration in females, and alterations in lipid metabolism. Conclusions: PAE negatively affects the development of the autonomic nervous system, resulting in decreased heart rate and altered sympathetic activity. PAE also induces cardiovascular abnormalities with sex-specific effects, highlighting a relationship between PAE consequences and sex. Elevated liver enzymes in the PAE group may indicate direct toxic effects, while increased creatinine levels, particularly in females, may suggest an influence on nephrogenesis and vascular function. The reduced potassium content may be linked to hypothalamus–pituitary–adrenal axis overactivity.