This work provides a detailed survey of the progress in dictionary-learning methods in the area of Automatic Target Recognition (ATR) systems, emphasizing the importance these techniques, in general, and the role of the methodologies in respect of growing accuracy and the efficiency, in particular, play in the tasks of identifying and classifying targets. Using an approach that combines literature review and bibliometric analysis, we unravel the history of dictionary learning in ATR, pointing out its interaction with machine learning algorithms, sparse representation, and radar imaging technologies. The core themes and innovations that shape the field are identified in the analysis aided by VOSviewer, with the integration of radar imaging and machine learning coming out as vital for developing efficient target recognition strategies. This study reveals a major trend of utilizing advanced computational models in dealing with complexities of modern surveillance and reconnaissance missions that helps increase operational efficiency in military as well as civilian applications. This review not only provides an overview of the current state of ATR research but also identifies potential developing lines, highlighting the crucial role of continuous improvement of computational algorithms and the interrelation between signal processing and machine learning for realization of unparalleled accuracy and efficiency in ATR systems.
This paper illuminates the scientific footsteps of the Short-Wave Infrared (SWIR) detectors, tracing the journey of the growth and development in this critical technology area from its early days in 1969 to its swift ascent in 2023. Employing bibliometric information and VOSviewer examination, we unveil major tendencies, thematic clusters, and the dynamic swelling of the scientific publications, spotlighting the burgeoning interest and technological evolution of SWIR detectors. Our research pieces together the key stages of SWIR technology emergence, originating from the initial revelations, advances in material science and also expansion of application spheres from military surveillance to environmental monitoring and medical imaging. Thus, we shed a light on the tremendous sway of SWIR detectors on the optical engineering and also photonics, attributable to their distinct capabilities to resolve current challenges. The findings throw a lot of light on the maturation within the SWIR detector research, underlining its heightening significance in both scientific and also industrial applications. This paper is a very comprehensive overview of the SWIR detector landscape that elaborates the successes of the past and also the trajectory of the future for the researchers, practitioners, and policymakers journeying in this transformative field.
KEYWORDS: Renewable energy, Signal filtering, Solar energy, Systems modeling, Sustainability, Data modeling, Analytical research, Tunable filters, Modeling, Electronic filtering
Within the process of development of sustainable energy solutions, the Ensemble Kalman Filter (EnKF) holds an allimportant key by assisting in forecasting and optimization of renewable energy systems. This essay describes in detail how the EnKF is utilized in diverse sectors of the renewable energy, of which one of its many vital roles is managing the variability and uncertainty that characterize wind and solar energy sources. By performing a meta-analysis and bibliometric work we discover that EnKF do two things very well – the level of the predictive model accuracy is increased and it is also easy to allocate the resources. Grid stability is another issue which EnKF solves well. The versatility of EnKF in wind forecasting has been highlighted in light of a study which has not only demonstrated how this method may be applied in renewable energy sources but also sheds light on recent developments as well. We will leave the forward part to researching potential additional studies such as EnKF integration with machine learning methods and its use towards renewables recent development. The work above shows how EnKF capable advanced data assimilation methods are highly needed for phasing out fossil fuels and ensure the shift to renewable energy sources as the global primary energy source.
This paper presents a comprehensive bibliometric analysis of the field of Autonomous Unmanned Systems (AUS), focusing specifically on genetic algorithm-based path planning, from 1997 to 2023. The study aims to map the evolution, trends, and future directions in this dynamic domain, highlighting the growing significance of genetic algorithms in the advancement of AUS. Our methodology integrates advanced bibliometric and text mining techniques, utilizing data from Scopus to provide both quantitative and qualitative insights. The analysis covers a corpus of 504 documents from 326 sources, revealing an increasing trajectory in research publications, particularly from 2010 onwards. This trend reflects the expanding academic and industrial interest in more sophisticated and efficient path planning methods for AUS. The paper identifies key thematic clusters, including optimization algorithms, energy and path efficiency, and communication technologies, emphasizing the necessity of interdisciplinary approaches in the field. Despite significant progress, challenges remain in safety, regulatory compliance, and enhancing the robustness and energy efficiency of path planning algorithms. The findings indicate a shift from foundational research to more applied and specialized areas, with potential new directions focusing on refining algorithms for specific applications and exploring integration with emerging technologies. Our study provides a comprehensive overview of the development of genetic algorithm-based path planning in AUS, offering valuable insights for future research directions. It underscores the importance of this field in various sectors and its potential for significant advancements in operational efficiency and effectiveness of autonomous unmanned systems.
This paper investigates the integration of the Kalman filter with fluorescence analysis in biomedical imaging, a synergy that holds the promise of advancing diagnostic accuracy and enhancing research methodologies in the study of biological systems. Employing a rigorous bibliometric analysis through VOSviewer, we explore the key trends, influential research clusters, and seminal publications that have marked the evolution of this interdisciplinary field. The Kalman filter, renowned for its predictive capabilities in real-time signal processing, emerges as a crucial tool for improving the signal-to- noise ratio in fluorescence imaging, thereby facilitating the extraction of more accurate and meaningful data from complex biological phenomena. Our analysis reveals a dynamic and growing research landscape, where methodological advancements and computational challenges intersect with practical applications in biomedical imaging. By highlighting the significant contributions and identifying areas ripe for future investigation, this study underscores the potential of Kalman filter-enhanced fluorescence analysis to revolutionize biomedical diagnostics and imaging, offering new insights into cellular and molecular processes. Through this synthesis, we aim to provide a comprehensive overview of the current state of the art and to chart a course for the next wave of innovations in the field.
This paper tackles the new utilization of acoustic tomography and synthetic data modeling to improve autonomous navigation systems, especially in environments where traditional navigation tools, such as GPS, are either restricted or absent. Thus, acoustic tomography, based on sound waves, as well as synthetic data modeling which creates unrealized but realistic navigation scenarios, seems to be a very useful way of enhancing accessible navigation in terms of reliability, precision, and adaptability. This research via a comprehensive literature review, review, and synthesis of current research articulates the promise of these technologies to transform navigation systems. It focuses on the approach in the integration of acoustic sensing with computational models in developing the system autonomy, covering the technological advancements and the problems. Besides, implications of the research to the whole range of applications from underwater exploration to urban autonomous vehicles specify the requirement of innovation and exploration development in this sphere. By providing a broad review and evaluation of the interactions of acoustic tomography with synthetic data modeling, this paper seeks to promote progress in autonomous navigation technologies, laying the groundwork for the future studies and engineering work in navigation through the world's most difficult environments.
This paper presents a thorough investigation into the convergence of Particle Swarm Optimization (PSO) and Multi-Disciplinary Design Optimization (MDO), two pivotal methodologies in the realm of computational optimization. By harnessing the strengths of PSO's heuristic search capabilities and MDO's integrative design approach, this study explores the synergistic potential of combining these methods to tackle complex optimization challenges. Through a systematic literature review and bibliometric analysis, we delve into the evolution, methodologies, applications, and outcomes of this interdisciplinary integration, drawing from a diverse array of scholarly works. Our analysis reveals a growing trend in the application of PSO within MDO frameworks, highlighting significant advancements, identifying gaps in the current literature, and suggesting fruitful directions for future research. The findings underscore the robustness and adaptability of PSO-MDO integration across various domains, offering insights into its potential to enhance optimization practices and contribute to the advancement of engineering and technology. This study not only charts the current landscape of PSO and MDO convergence but also sets the groundwork for future explorations in this promising research domain.
Over the past few years, the number of CubeSats has been steadily growing, allowing them to be used successfully for many innovative and complex missions requiring much larger satellites. As a result, the space sector has seen a breakthrough in efficiency while reducing costs for designing, testing, and integrating these devices. In collaboration with Istanbul Technical University (ITU) and Sabanci University (SU), the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST) has developed Sharjah-Sat-1, the first CubeSat mission of the University of Sharjah. This 3U+ CubeSat was launched on January 3, 2023, with two payloads on board. In addition, Sharjah-Sat-1 is dedicated to building the capacities of SAASST and exposing the students of the University of Sharjah to the world of space technology through a space engineering program. This paper will provide an overview of the dual camera system and a description of the tests conducted to ensure mission success. Following this, we will discuss in more detail how the images and data obtained from the camera were used to study the terrain in that region.
The space sector's rapid growth, coupled with increased accessibility to space, has led to the popularity of miniaturized satellites known as CubeSats. These cost-effective and agile nanosatellites have gained international recognition in government, education, and private sectors. CubeSats, standardized at 10 cm x 10 cm x 10 cm, come in various sizes (1U, 2U, 3U, and 6U) and are preferred by the GIS/RS community for earth observation capabilities. Sharjah Academy for Astronomy, Space Science and Technology (SAASST) in the UAE has established a CubeSat laboratory, launched the Sharjah-Sat-1 (3U+) and now embarking on the Sharjah-Sat-2 mission. Sharjah-Sat-2 is a 6U CubeSat equipped with an advanced high-definition hyperspectral camera, Hyperscape100, to enhance infrastructure projects and establish an early warning system for environmental phenomena. This paper will discuss advancements in spaceborne hyperspectral imagers, compare nanosatellites to larger satellites, highlight the Sharjah-Sat-2 project, and explore its positive impact on the GIS/RS community.
This paper investigates the integration of Kalman Filter technologies into modern agricultural practices, with a focus on advancing crop monitoring techniques. Through a comprehensive bibliometric and text mining analysis, we explore the evolution of research in this area from 1983 to 2023, highlighting the steady growth in scholarly publications and the interdisciplinary nature of the field. Our findings reveal a significant interest in leveraging Kalman Filter algorithms to enhance the precision and efficiency of agricultural operations, addressing the challenges of sustainability and food security. The study underscores the importance of data assimilation, real-time monitoring, and predictive analysis in agriculture, facilitated by the adoption of Kalman Filter and its variants. We also examine the integration of emerging technologies, such as Unmanned Aerial Vehicles (UAVs) and remote sensing, with Kalman Filter techniques to develop sophisticated agricultural monitoring systems. The paper concludes with insights into future research directions, emphasizing the need for overcoming barriers to technology adoption and fostering interdisciplinary collaborations. This work contributes to the understanding of how computational intelligence can transform agricultural practices, offering solutions for more sustainable and efficient farming.
This research plunges into the rapidly growing radar technology in the medical sector, putting emphasis on its possibility to revolutionize elderly care and health monitoring among the aging global population. Based on a systematic literature review and rigorous bibliometric analysis, we discuss radar technology application in healthcare, focusing on its potential for non-invasive, high-accuracy diagnosis and continuous patient monitoring. Our findings highlight the critical harmony between radar technology and the advances in machine learning, artificial intelligence, and data analytics, which open the door to smart healthcare solutions. These advancements will improve early disease detection, fall risk prevention, and real-time health monitoring, resulting in quick medical responses. This study endeavors to offer useful knowledge to researchers, practitioners, and policymakers who are working towards the use of technology for better health in the context of the demographic changes that the world is experiencing in terms of an ageing population by mapping the current research landscape, identifying the existing trends and gaps, and proposing the future direction of research.
The concept of an autonomous rover system to perform maintenance, investigations, and data collection in remote or inaccessible locations has seen an increased demand recently. In this work, an autonomous rover is developed to detect radioactive contamination. The rover utilizes a gas tube radiation detector as an active sensing element and onboard modules to command and control the rover, such as a GNSS receiver, Autopilot controller, and a microcontroller as an onboard controller a communication module. The rover could be controlled by a human operator or autonomous control. In both cases, the operator would be far away from the scene. The rover has many potentially valuable applications, such as radiometric survey and mapping, locating survivors, or aiding in recovering victims after a CBRN disaster. This paper discusses the concept of operations and the design of the autonomous rover.
KEYWORDS: Biomimetics, Sensors, Prototyping, Servomechanisms, Design and modelling, Muscles, Microcontrollers, Signal detection, 3D printing, Electromyography
Bionic limbs have transformed the lives of individuals with missing or damaged limbs, enabling them to regain independence using electronic sensors and motors. Over the years, significant advancements have been made in prosthetic devices, with some reaching a level of sophistication that is almost indistinguishable from natural limbs. However, not all amputees have equal access to cutting-edge technology, which motivates the research and development presented in this paper. In this study, we have designed and developed a bionic arm that can be easily manufactured using additive manufacturing, paired with a wearable sensor suit that commands the actuators to execute movements. The use of gesturecontrolled wearable sensors allows for the creation of sophisticated bionic arms with applications in both civilian and military contexts. Furthermore, the team is exploring the use of advanced computer algorithms to enable fast and fluid movements, facilitating the performance of complex tasks with prosthetic limbs. This paper provides a general design overview of the bionic arm and its sensor suit, showcasing the potential of this innovative approach in revolutionizing the field of prosthetics. The use of additive manufacturing and wearable sensor technology opens up new possibilities for providing accessible and advanced prosthetic solutions for individuals with limb loss.
Sharjah-Sat-2 is a 6U Earth Observation (EO) CubeSat currently being developed by the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST) and the University of Sharjah (UoS). The 6U CubeSat is currently being designed and integrated with two payloads on board: (1) a High-Resolution Hyperspectral Imager with less than 5 Meters of Ground Sampling distance (GSD) and (2) an experimental payload consisting of a GNSS receiver. The mission's primary scientific objective is to capture High-Resolution Hyperspectral images of the United Arab Emirates to provide a constant stream of reliable data that can be utilized to improve the country's infrastructure and resource management. The secondary mission objective is to monitor and validate the integrity of GNSS signals received by CubeSat and compare them to calibrated ground based GNSS receivers. This paper will provide insight into the GNSS Payload onboard Sharjah-Sat-2 and how its data could be utilized to measure and validate the signal integrity of the observed GNSS satellites. Also, we will compare the observations with those made at the groundbased reference GNSS station available at our research facility.
KEYWORDS: Space weather, X-ray detectors, Satellites, Space operations, Situational awareness sensors, Aerospace engineering, Solar processes, Atmospheric particles, X-rays, Sun
Sharjah-Sat-1 is the first CubeSat to be designed and integrated at the Sharjah Academy for Astronomy, Space Sciences and Technology (SAASST), a research institute under the University of Sharjah (UoS) in the United Arab Emirates, with an active collaboration with Istanbul Technical University and Sabanci University in Turkey. The mission is due to launch in December 2022. Sharjah-Sat-1 hosts a primary payload of an improved X-Ray Detector (iXRD). The iXRD utilizes a CdZnTe crystal as an active detector to detect and measure bright and hard X-Ray sources and a tungsten collimator. The instrument’s detection range is 20-200 KeV at a spectral resolution of 6 Kev at 60 KeV [1]. The detector will be able to measure the flux of ionizing x-ray around the south Atlantic anomaly, the data of which will be shared to provide space situational awareness for other satellite operators to perform any preventative maneuvers to protect their space assets. This paper will discuss how the improved X-Ray Detector (iXRD) on-board the Sharjah-Sat-1 CubeSat can be utilized to provide space situational awareness.
Sharjah-Sat-2 is a 6U Earth Observation (EO) CubeSat currently being developed by the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST) and The University of Sharjah (UoS). The 6U CubeSat design is currently being Designed and Integrated with two payloads on board: (1) a High-Resolution Hyperspectral Imager with less than 5 Meters of Ground Sampling distance (GSD) and (2) an experiential payload consisting of a GNSS receiver. The mission’s primary scientific objective is to capture High-Resolution Hyperspectral images of the United Arab Emirates to provide a constant stream of reliable data that will be utilized to improve the country’s infrastructure and resource management. The secondary mission objective is to monitor and validate the integrity of GNSS signals. This paper will provide insight into the preliminary mission design of the Sharjah-Sat-2 Microsatellite, highlighting the mission’s payload, orbit determination, and coverage study.
Sharjah-Sat-1 is currently being developed as a collaborative research project between the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST), the University of Sharjah, Istanbul Technical University, and Sabanci University. A 3U CubeSat design has been adopted with a dual payload onboard: (i) the improved X-ray Detector (iXRD) and (ii) a system of two optical cameras. The mission's primary scientific target is the observation of bright, hard X-ray sources in our Galaxy and the solar coronal holes. The paper discusses a high-level design, testing, and validation of the mission's primary science payload. The iXRD (Improved X-ray Detector) is equipped with a pixelated CdZnTe-based crystal as the active detection material and a Tungsten collimator with a field of view of 4.26 degrees. The detection range is from 20 to 200 keV with a target spectral resolution of 6 keV at 60 keV. The paper will cover a high-level design of the iXRD, environmental testing performed on the detector such as thermal-vacuum and vibration testing, anSharjah-Sat-1 is currently being developed as a collaborative research project between the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST), the University of Sharjah, Istanbul Technical University, and Sabanci University. A 3U CubeSat design has been adopted with a dual payload onboard: (i) the improved X-ray Detector (iXRD) and (ii) a system of two optical cameras. The mission's primary scientific target is the observation of bright, hard X-ray sources in our Galaxy and the solar coronal holes. The paper discusses a high-level design, testing, and validation of the mission's primary science payload. The iXRD (Improved X-ray Detector) is equipped with a pixelated CdZnTe-based crystal as the active detection material and a Tungsten collimator with a field of view of 4.26 degrees. The detection range is from 20 to 200 keV with a target spectral resolution of 6 keV at 60 keV. The paper will cover a high-level design of the iXRD, environmental testing performed on the detector such as thermal-vacuum and vibration testing, and calibrating the detector.
Many components of our STELLA telescopes located on Tenerife, which were built by Halfmann in the 2000s have reached the end of their life with no replacement parts available. A solution was necessary to guarantee continuous operation and support for the next ten years. The prerequisite for the retrofit, however, was that the mechanical components remain largely untouched in order to simplify the upgrade. We decided to remove all the existing electronics in the main control cabinet. In order to avoid electronic interference in the scientific instruments, we took several precautions. This included an isolating transformer, line filters and power chokes for the servo drivers. All of the control electronics as well as the sensory inputs is now handled by Beckhoff components. A Beckhoff PLC CX5140 is the new ”electronic brain” replacing a Linux computer running the telescope control firmware. The new telescope control firmware written in TwinCAT3 is available as open source. MQTT messages are used to command the telescope and report sensor values and position information. Sensor measurements and the state of the telescope are logged in an Influx∗-database and visualized using Grafana†. Future enhancements include an improved guiding of the telescope using machine vision and a GigE camera in a closed loop on the PLC.
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