This study presents an assessment of concurrently identifying the best location and size of distributed generators (DGs), shunt capacitors (SCs), and electric vehicle charging stations (EVCSs) in optimally reconfigured radial distribution networks (RDNs). A comprehensive literature review indicates that this multi-unit combination has the potential to enhance RDN performance significantly, but it remains an underexplored area of research. Therefore, further in-depth investigation is necessary to understand and fully maximize the benefits of this method. The optimal placement and sizing (OPS) of the mentioned multi-unit in RDNs is realized by employing a metaheuristic optimization technique subject to the fulfillment of a well-defined fuzzified-objective function comprising of line losses reduction, power factor improvement, voltage deviation reduction, and DG penetration limit. Employing the concept of centroid-based oppositional learning (COL), an improved version of the artificial hummingbird algorithm (AHA), named COLAHA, is proposed to decipher the adopted issue. The results achieved utilizing the offered approach are matched with those of the additional innovative algorithms such as the basic AHA, arithmetic optimization algorithm, genetic algorithm, and whale optimization algorithm. By evaluating it against several benchmark functions, the effectiveness of the proposed COLAHA is established. The performance of the aforementioned studied algorithms is further tested to find the OPS of DGs, SCs and EVCSs in the standard IEEE 69- and 118-bus RDNs. Results obtained conclude that the COLAHA has offered quick convergence and the best results over the others for all the studied combinations of the multi-unit model.

The dual-tree complex wavelet transform (DTCWT) solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT). It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG), are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT) with a detection rate of 4.5% to 15.8% higher depending on the fabric type.

In the wake of the increasing integration of cyber-physical systems within power grids, ensuring the security and stability of these systems has become paramount. This paper presents a comprehensive security analysis of cyber-physical power systems (CPPS) employed for frequency control in deregulated power markets. In the era of evolving power grids, characterized by increasing renewable energy penetration and market deregulation, maintaining frequency stability becomes a critical challenge. To address this challenge, this study investigates the synergistic utilization of Unified Power Flow Controller (UPFC) device and Redox Flow Battery (RFB), to enhance the frequency control performance. A realistic two-area CPPS is considered where a cascaded Fractional-Order Proportional-Integral-Derivative-Fractional Integral (FOPID-FI) controller has been implemented for frequency control in both areas. Our analysis delves into the vulnerabilities inherent in CPPS, considering potential cyber threats and their impact on frequency control mechanisms. By employing a systematic approach, we identify critical points of vulnerability in the system architecture and propose countermeasures to mitigate cyber threats effectively. We include extensive simulation results to validate the efficacy of the proposed work by analyzing the performance of the system from vital perspective viz., sensitivity, scalability, robustness, stable, and resilient.

Breast cancer is the most common malignancy in women globally, stemming from gene mutations that prompt irregular cellular growth and subsequent tumor development. Early-stage detection of cancer cells results in a remarkable 99% survival rate. This research presents a microwave imaging technique for the non-invasive identification of tumors in the initial stages within the women’s breast. A low-cost antenna array with an Artificial Magnetic Conductor (AMC) is proposed, featuring a compact structure size of 37.2 \(\times\) 37.2 mm\(^{2}\). The AMC, a metamaterial, acts as a reflective surface to enhance frequency selectivity, specifically at 8.48 GHz. The maximum gains reached 9.35 dBi in simulated results and 10.51 dBi in measured results. The fabricated antenna validates the simulated findings, and its operational efficiency has undergone experimental validation. Moreover, fidelity factors in face-to-face (FtF) and side-by-side (SbS) scenarios are delineated. The antenna, operating as a transceiver, is applied to a modeled breast phantom across five distinct cases for numerical simulations pertaining to cancer cell detection applications. The outcomes of this research bear considerable implications for advancing early-stage breast cancer detection methodologies.