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In recent days, stress is a major phenomenon that adversely affects both individuals and communities. The research in computing the stress factor has wider advantages as it improves personal learning, learning operations, and high productivity that benefits society. Several computational techniques come into concern to avoid and reduce the stress level using the electrocardiogram (ECG) signals. In this study, the stress level was classified using the feature extraction approach in combination with the classifier. The signal is processed using the variational mode decomposition denoising technique to reconstruct the original signal. The decomposed signal was further extracted using the time–frequency domain technique as characteristics of the ECG signal such as R-wave and T-wave constructed. Further, the support vector machine classifier was used to classify the stress level (low, medium, and high) of the extracted signal. Based on stress classification outcomes, the robot offers a range of personalized interventions to users. These interventions include relaxation exercises, deep breathing techniques, or guided mindfulness sessions. The average accuracy obtained using the proposed technique is 98.98% but without using the feature extraction technique, it is 97.71%. The other performance parameters also get improved and the results are finally compared with the existing techniques.

The digitization of important documents and their segregation can be a beneficial and time-saving activity as individuals will have greater access to important documents and will be able to use them in regular tasks as well as endeavours. In recent years, research into the application of deep networks in robot systems has increased as a direct consequence of the advancements made in classification algorithms over the past few decades. Robotic vision automation for the segregation of sensitive and non-sensitive documents is required for many security concerns. The methodology of this article is initially focused on the identification of a good computer vision-based technique for the classification of sensitive documents from non-sensitive documents. The authors first identified the standard parameters in terms of reliability, loss, precision, and recall by employing deep learning techniques, such as neural networks with convolutions and transfer learning (TL) algorithms. The extraction of features based on pre-trained deep learning models was referenced in numerous publications. Similarly, we applied most of the feature extraction techniques to identify feature extraction from the images. Then, these features were classified by machine and ensemble learning models. However, the pre-trained models-based feature extraction along with machine learning classification resulted better in comparison to the deep learning and TL procedures. Further, the better-identified techniques were applied as the brain behind the vision of a robotic structure to automate the segregation of sensitive documents from non-sensitive documents. This proposed robotic structure could be applied when we have to find some specific and classified document from the haystack.

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities. [ABSTRACT FROM AUTHOR]

A low profile, small size and lightweight dual-band antenna prototype is presented for satellite application (Ku-band). The proposed design meets the spectrum necessity of region 3 specified by International Telecommunication Union (ITU). The proposed antenna achieves a frequency band of 11.69–13.24 GHz and 13.72–15.07 GHz for direct broadcast service (DBS) and fixed satellite service (FSS) in receive mode, and FSS in transmit mode, respectively. The antenna achieves the impedance bandwidth of 12.29%, and 9.37%, for first, and second band of frequency, respectively. In addition, the axial ratio of 160 MHz (12.30–12.46 GHz) is achieved by the design. The proposed design is fabricated and measured for various parameters such as reflection coefficient, bandwidth, gain and radiation pattern. Moreover, the complexity in realising the design is less due to its single patch and a single layer. [ABSTRACT FROM AUTHOR]