In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach’s superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems.

This research work examines the reliability of a wireless communication system that uses a hybrid radio frequency (RF) and underwater optical wireless communication (UOWC) in a dual-hop system. The proposed system consists of a single source (S) node that sends information to a destination (D) node through an amplify-and-forward (AF) relay (R) node using a multiple-input multiple-output (MIMO)-RF and UOWC links. The channel between the (S) node is subject to Nakagami-m fading using a transmit antenna selection (TAS) technique and maximal ratio combining (MRC) at (R) node, and exponential generalized gamma distribution (EGG) at (D) node to evaluate the overall statistical analysis of the proposed end-to-end(E2E) system. The proposed system has been extensively tested and validated under various conditions using a novel cumulative distribution function(CDF) and probability density function(pdf), and the paper provides closed-form expressions for the outage probability (OP) and average bit error rate (BER) for each channel model.

Disruption of a workstation is the major contributor to the downtime in any manufacturing process, and it impacts the performance, cost, consumer’s centric value, and efficiency. The effect cascaded in the whole assembly line by blockage and starvation caused by defective workstations. Consequently, the key to appropriately allocating resources and limiting downtime is an accurate understanding of the aftermath of each disturbance. The work presented here investigates how disruptions at a single workstation can affect nearby workstations. The researchers modified the Alarm Trip Point (ATP) by setting a threshold value for a malfunctioning workstation. This can increase or decrease the ATP depending on the state of the workstation. By using this strategy, downtime and batch rejection can be reduced. The researchers then extended their study from a single workstation to multiple workstations, creating a more general model. They deployed their strategy to an asphalt processing plant as an experimental case study in order to illustrate how effective it is in decreasing downtime, frequent starts and stops, and numerous rejections. The proposed approach in the study resulted in a decrease in the number of starts and stops, downtime, and a number of rejections by a significant amount. The number of start-stop events was reduced by 60%, resulting in a significant decrease in the number of rejections and downtime, and finally increasing the consumer-centric value and efficiency. The study also presented a comparison of the proposed approach with the ATP approach by Yu et al., which showed that the proposed approach outperformed the ATP approach in terms of reduction in downtime and rejection.

This paper proposes a run-time reconfigurable butterfly structure capable of simultaneously performing Radix-8, Radix-4, Radix-3, and Radix-2 Fast Fourier Transform (FFT) computations to cater to the bandwidth requirements of current Long-Term Evolution (LTE), Fifth Generation (5G), and beyond systems. While LTE and 5G promise exceptionally high throughput, ranging from 300 Mbps to 3 Gbps, Field-Programmable Gate Array (FPGA) and Application Specific Integrated Circuit vendors face significant challenges in meeting these demands. Efficient implementation strategies are essential for realizing these standards in silicon. Given that FFT is the cornerstone of 5G systems, its computational complexity poses challenges in meeting high throughput requirements. Furthermore, the flexibility to adapt to different bandwidths is crucial in 5G networks. Therefore, a configurable FFT engine that optimizes resource utilization while providing high throughput is highly desirable. This paper presents two FFT architectures based on the proposed reconfigurable butterfly, offering designers the flexibility to choose the most suitable approach for realizing efficient and high-throughput solutions tailored to the requirements of 5G and beyond.