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This study aims to evolve an enhanced butterfly optimization algorithm (BOA) with respect to convergence and accuracy performance for numerous benchmark functions, rigorous constrained engineering design problems and an inverse synthetic aperture radar (ISAR) image motion compensation.

Adaptive BOA (ABOA) is thus developed by incorporating spatial dispersal strategy to the global search and inserting the fittest solution to the local search, and hence its exploration and exploitation abilities are improved.

The accuracy and convergence performance of ABOA are well verified via exhaustive comparisons with BOA and its existing variants such as improved BOA (IBOA), modified BOA (MBOA) and BOA with Levy flight (BOAL) in terms of various precise metrics through 15 classical and 12 conference on evolutionary computation (CEC)-2017 benchmark functions. ABOA has outstanding accuracy and stability performance better than BOA, IBOA, MBOA and BOAL for most of the benchmarks. The design optimization performance of ABOA is also evaluated for three constrained engineering problems such as welded beam design, spring design and gear train design and the results are compared with those of BOA, MBOA and BOA with chaos. ABOA, therefore, optimizes engineering designs with the most optimal variables. Furthermore, a validation is performed through translational motion compensation (TMC) of the ISAR image for an aircraft, which includes blurriness. In TMC, the motion parameters such as velocity and acceleration of target are optimally predicted by the optimization algorithms. The TMC results are elaborately compared with BOA, IBOA, MBOA and BOAL between each other in view of images, motion parameter and numerical image measuring metrics.

The outperforming results reflect the optimization and design successes of ABOA which is enhanced by establishing better global and local search abilities over BOA and its existing variants.

This paper aims to present an approach of utilizing multiple-input multiple-output (MIMO) radar concept to enhance pedestrian classification in automotive sensors. In a practical environment, radar signals reflected from pedestrians and slow-moving vehicles are similar in terms of reflecting angle and Doppler returns, inducing difficulty for target discrimination. An efficient discrimination between the two targets depends on the ability of the sensor to extract unique characteristics from each target, for example, by exploiting Doppler signatures. This study describes the utilization of MIMO radar for Doppler measurement and demonstrates its application to improve pedestrian classification through actual laboratory measurements.

Multiple non-modulated sinusoidal signals are transmitted orthogonally over a MIMO array using time division scheme, illuminating human and non-human targets. The reflected signal entering each of the receiving antenna are combined at the radar receiver prior to Doppler processing. Doppler histogram was formulated based on a series of measurements, and the Doppler spread of the targets was determined from the histograms. Results were compared between MIMO and conventional single antenna systems.

Measurement results indicated that the MIMO configuration provides able to capture more Doppler information compared to conventional single antenna systems, enabling a more precise discrimination between pedestrian and other slow-moving objects on the road.

The study demonstrated the effectiveness of using MIMO configuration in radar-based automotive sensor to enhance the accuracy of Doppler estimation, which is seldom highlighted in literature of MIMO radars. The result also indicated its usefulness in improving target discrimination capability of the radar, through actual measurement.

Synthetic aperture radar exploits the receiving signals in the antenna for detecting the moving targets and estimates the motion parameters of the moving objects. The limitation of the existing methods is regarding the poor power density such that those received signals are essentially to be transformed to the background ratio. To overcome this issue, fractional Fourier transform (FrFT) is employed in the moving target detection (MTD) process. The paper aims to discuss this issue.

The proposed MTD method uses the fuzzy decisive approach for detecting the moving target in the search space. The received signal and the FrFT of the received signal are subjected to the calculation of correlation using the ambiguity function. Based on the correlation, the location of the target is identified in the search space and is fed to the fuzzy decisive module, which detects the target location using the fuzzy linguistic rules.

The simulation is performed, and the analysis is carried out based on the metrics, like detection time, missed target rate, and MSE. From the analysis, it can be shown that the proposed Fuzzy-based MTD process detected the object in 5.0237 secs with a minimum missed target rate of 0.1210 and MSE of 23377.48.

The proposed Fuzzy-MTD is the application of the fuzzy rules for locating the moving target in search space based on the peak energy of the original received signal and FrFT of the original received signal.

With the continuous development of aerospace technology, space exploration missions have been increasing year by year, and higher requirements have been placed on the upper level rocket. The purpose of this paper is to improve the ability to identify and detect potential targets for upper level rocket.

Aiming at the upper-level recognition of space satellites and core components, this paper proposes a deep learning-based spatial multi-target recognition method, which can simultaneously recognize space satellites and core components. First, the implementation framework of spatial multi-target recognition is given. Second, by comparing and analyzing convolutional neural networks, a convolutional neural network model based on YOLOv3 is designed. Finally, seven satellite scale models are constructed based on systems tool kit (STK) and Solidworks. Multi targets, such as nozzle, star sensor, solar,etc., are selected as the recognition objects.

By labeling, training and testing the image data set, the accuracy of the proposed method for spatial multi-target recognition is 90.17%, which is improved compared with the recognition accuracy and rate based on the YOLOv1 model, thereby effectively verifying the correctness of the proposed method.

This paper only recognizes space multi-targets under ideal simulation conditions, but has not fully considered the space multi-target recognition under the more complex space lighting environment, nutation, precession, roll and other motion laws. In the later period, training and detection can be performed by simulating more realistic space lighting environment images or multi-target images taken by upper-level rocket to further verify the feasibility of multi-target recognition algorithms in complex space environments.

The research in this paper validates that the deep learning-based algorithm to recognize multiple targets in the space environment is feasible in terms of accuracy and rate.

The paper helps to set up an image data set containing six satellite models in STK and one digital satellite model that simulates spatial illumination changes and spins in Solidworks, and use the characteristics of spatial targets (such as rectangles, circles and lines) to provide prior values to the network convolutional layer.

The purpose of this paper is to examine existing radar sensor results, techniques for through‐wall radar and current applications for the technology.

The paper provides information on sensing through a high‐attenuation obstacle and the associated pitfalls and considerations. Results from ultra‐wide‐band (UWB) impulse radar, micro‐Doppler radar, and synthetic aperture radar (SAR) targeted at this area are presented. Discussion of radar clutter classification is given and also observations on presenting a system with a non‐zero false alarm rate to a user to give best confidence and maximum decision capability.

There are significant new requirements for through‐wall radar which a combination of UWB, continuous wave, and SAR techniques with recent signal processing advances and the advent of low‐cost radio and image processing can meet in distributed markets. Risk of a poor user level decision in a non‐zero‐false‐alarm‐rate system can be mitigated by increasing the number of inputs into the decision.

The paper lists challenges that have been overcome in the area of through‐wall sensing and presents results from novel radar sensors.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

India and the Republic of Korea are two prominent democracies in Asia. Both countries had to fight for their long-desired freedom. India's growing friendly relationship with the Republic of Korea has been marked by mutual understanding and bilateral trading cooperation. India–Republic of Korea relations have made great strides in recent years and have become truly multidimensional, spurred by a significant convergence of interests, mutual goodwill, and high-level knowledge exchanges. This study intends to critically discuss how soft power has been applied in New Delhi–Seoul relations and how soft power has been a very effective tool to maintain unity among the Indian diaspora and the Korean community. Soft power has been beneficial for India in propagating India's films, culture, medicines, yoga, heritage, etc., through which India is generating revenue. The blending of liberal economic policy and knowledge-based soft power diplomacy has immensely helped in making reciprocal bilateral relations. South Korea's open market policies found resonance with India's economic liberalization and “Look East Policy” as well as “Act East Policy.” Similarly, India has opened up its economy through the adoption of “new economic policy.” With the trade liberalization, India had started vibrant trading relations with South Korea. The significant investment of Korean companies in India has made a strong base of economic relations. Both countries have developed their knowledge exchange programs in many ways.

This chapter examines the concepts of race and racism, critically reviewing their historical and contemporary applications in everyday life as well as in academic and policy debates. Racism has been extensively researched, with various theories and conceptualisations developed across social science. However, there is a great deal of disagreement regarding its nature, contemporary significance and empirical validation. This chapter examines these and attempts to synthesise some of the common definitions of racism provided in the literature. It explores related concepts and underlying themes pertaining to expressions of race and racism. Furthermore, it unpacks current knowledge about racial issues and discusses recent advances in the conceptual understanding of various forms of racism. It also elucidates the social, political and analytical applications of racism as a concept and the significance of racism in contemporary societies. The chapter concludes by highlighting how racism is a dynamic phenomenon, continuously evolving with the social, political and technological transformations in contemporary societies.