Different image segmentation algorithms are used for real-time applications like autonomous vehicles, robotics, disaster management, etc. Because of the computational complexity of these algorithms, hardware realizations are cumbersome and complicated. The frames per second achieved are barely sufficient for accurate perception of the problem at hand. The next important challenge is the implementation of evolutionary clustering algorithms like genetic algorithm for improved accuracy, after introducing some simplifying techniques to make the ensuing hardware quicker, less complex with improved power consumption and hardware area/size. Bioinspired algorithms are an excellent candidate solution in this regard. It can be implemented in an FSM based approach to detect faults in the centroid initialization phase like detecting zero data members within a cluster. To further reduce the complexity challenges of existing algorithms while maintaining good accuracy, some new bio-inspired algorithms like roller dung beetle clustering have also been tested.
Top1. Introduction
Optimization methods, AI, Machine Learning and Blockchain Techniques are heavily applied in Aerospace Engineering. Machine Learning offers the capability of improving from experience and employing novel self/reinforced-learning methods which can automate many aerospace systems and applications. Machine learning methods have been successfully applied to provide us with valuable insights from air traffic data, flight scheduling information, geographical information, navigation and path planning data, etc. Despite the incredible utility of various optimization techniques, various unexpected security problems have been demonstrated in recent years. Several challenges and multidimensional problems faced by aero-systems can be resolved by the judicious application of cutting-edge AI machine and deep learning algorithms combined with a vast arsenal of optimization techniques.
Nature from time immemorial has been an inspiration for man. She has inspired us to mimic her ways in many meta-heuristic techniques, algorithms and architectures, especially while designing engineering solutions. Operations performed by these are simple but powerful and remarkable ideas can dramatically improve the performance, efficiency, economy and accuracy of the system. Science and Engineering owes a lot to nature. From the Newton’s gravitational law to the design of modern buildings, nature has been nourishing human brain with infinite supply of ideas, worth mimicking. Every time Nature comes up with great inspirations and revelations which can revolutionize the technology dramatically of that period. The best examples are airplanes inspired from birds, ships and submarines inspired from aquatic organisms, camera system inspired by human eye, humanoid robots to…Velcro to geko, the list is endless. Even the vast array of Bio-Inspired algorithms and Nature-inspired architectures from- Fibonacci pattern seen in the flowers and shells, to the billions of the connections of the Neural networks termed as Deep learning architectures are nothing but gifts of nature, envisioning those people who tries to approach her from time to time. The profession of Engineering demands a lot of problem solving along with achieving optimal outcomes within a limited period, that too in an economical way. So, Engineers, at times are forced to look outside ‘to see and learn how the nature solves the similar problem and how he can replicate the same idea in their designs.’ The field of engineering called as biomimetics (biomimicry) implying designs where engineering solutions mimics Nature is growing very rapidly, thanks to Nature.
Satellite images, once cryptic mosaics of pixels, are now yielding their secrets to the power of clustering algorithms in aerospace engineering. These algorithms, akin to celestial cartographers, group similar pixels together, carving the image into meaningful segments, each revealing a piece of the cosmic puzzle. Imagine a vast expanse of ocean captured by a satellite camera. Traditional analysis might struggle to distinguish subtle variations in water color, potentially missing crucial details about currents, phytoplankton blooms, or pollution. But clustering algorithms step in, identifying clusters of pixels with similar spectral signatures. These clusters could then reveal hidden eddies, track the drift of pollutants, or even map the distribution of marine life. Beyond Earth's watery embrace, clustering algorithms tackle the Martian landscape with similar finesse. By grouping pixels based on their brightness, texture, and temperature, they can delineate rock formations, unearth potential ice deposits, and even identify signs of ancient riverbeds. This information is invaluable for planning future robotic missions and piecing together the Red Planet's geological history. The applications extend beyond planetary exploration. Clustering algorithms play a crucial role in Earth observation, segmenting satellite images to distinguish urban sprawl from natural vegetation, monitor deforestation, and track the movement of glaciers. This information empowers policymakers to make informed decisions about land management (Sasaki et al., 2007), environmental protection (Cao et al., 2018), and disaster preparedness (Amit et al., 2017).