A novel Radio Frequency Identification (RFID) based sensor supporting touch detection and localization features is proposed in this work. The formulated sensor leverages chipless variant of RFID technology for the design of a passive fully-printable frequency domain-based sensor-incorporated tag. The sensor is composed of square resonators arranged in a peculiar fashion laid down across a 3 × 2 grid. The proposed sensor incorporated-tag readily keeps track of human-digit position, allowing for tracking of finger-swipes which, in turn, can potentially be used for recognition of unlock patterns and security codes. Performance of the sensor is analyzed using its Radar Cross Section (RCS) response observable in the spectral domain. Each constituent resonant-element making up the sensor resonates at a single frequency represented by a distinct dip in the RCS response. The spectral dip drifts well outside of its allocated band upon occurrence of a touch event. A functional prototype of the sensor tag is fabricated on a 0.508 mm thick Rogers RT/Duroid® 5880 laminate is scrutinized of its electromagnetic performance. The sensor possesses a compact physical footprint equal to 45 mm × 55 mm. The obtained results solidify the suitability of the proposed sensor for deployment in secure access control settings prevalent in smart cities and connected home applications.

In this article, we propose a novel feature selection method based on hybrid neural networks for emotional classification avoiding expensive computation. With the development of neural networks for machine learning tools, the Electroencephalogram (EEG)-based classification of human emotions is increasingly important in providing health-care for Edge Computing in support of multiple Internet of Things (IoT) applications. However, classifying emotions through EEG signals is very challenging due to the low temporal boundaries and non-linear nature of EEG signals. Regarding non-linearity, we propose a hierarchical Semi-skipping Layered Gated Unit (SLGU) besides Efficient Network (ENet) for feature extraction. For faster processing, we have introduced a semi-skipping layer for Gated Recurrent Units (GRU) in Recurrent Neural Networks (RNN). The entered layer automatically skips the divergent factor during network training. Preprocessed EEG signals are sent to the hybrid SLGU-ENet model for deep feature extraction. To overcome the computational cost, an optimal function reduction method called the Bag of Visualized Characteristics (BoVC) is used. The entire facility is verified against two publicly available datasets. The results show that the classification performance of the proposed model achieves superior classification precision in a short processing time compared to the state-of-the-art models. The proposed algorithm required around 1.2-5 seconds, suitable for real-time IoT applications.

A polarization insensitive, compact, fully-passive bit encoding structure exhibiting 1 : 1 resonator-to-bit correspondence is presented. Inspired by frequency selective surface (FSS) based microwave absorbers, the structure readily operates as a chipless radio frequency identification (RFID) tag. The unit cell is composed of several concentric hexagonal loops. Finite repetitions of the unit cell constitute the proposed RFID tag in its entirety. The required bit sequence is encoded in the frequency domain by addition or omission of corresponding nested resonant elements. A functional prototype is fabricated on a commercial-grade grounded FR4 substrate, occupying a physical footprint of 23 × 10 mm² while offering a capacity of 14 bits. The proposed tag boasts a minuscule profile, and demonstrates polarization insensitivity as well as stable oblique angular performance.

Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identified based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass filter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method.

Swarm intelligence algorithms, based on the behaviors of natural organisms, provide efficient solutions to complex optimization problems. This chapter explores the Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO), which are widely employed in engineering optimization. The chapter begins with an overview of fundamental swarm intelligence concepts, such as self-organization and emergent behavior, followed by detailed explanations of the working principles of WOA and GWO. Real-world engineering applications, such as wireless network resource allocation, power control in wireless networks, and optimization of Quality of Service (QoS) in wireless networks, are explored, showcasing how these algorithms have been successfully implemented to solve complex, real-world challenges. The chapter also examines the strengths and limitations of WOA and GWO, offering a comparative analysis to other optimization methods. Finally, the potential future applications of swarm intelligence, including bio-hybrid systems and quantum optimization, are discussed, highlighting emerging trends in the field.