The radiation pattern of radio frequency identification (RFID) antennas is influenced by various factors such as design, operating frequency, and polarization, which determine characteristics like directionality, omnidirectionality, beam width, and gain. Achieving precise readings over extended distances is crucial for the effectiveness of RFID systems, enabling faster item retrieval and delivery. The read distance, a critical aspect of RFID system performance, depends on factors like transmitted power, frequency, and antenna gain. Passive backscatter RFID setups particularly benefit from optimizing read distance for efficient operation. Fuzzy logic, as a soft computing technique, addresses uncertainties inherent in RFID systems effectively. This paper presents a novel approach to RFID antenna design, utilizing fuzzy logic to dynamically adjust frequency and power transmission. By enhancing field distribution, polarization, and received signal strength, this approach aims to optimize tag readings at extended distances, thereby improving overall system effectiveness. The methodology involves implementing algorithms in a C program to control the long-range distance aspects of the RFID system. Incorporating fuzzy rule algorithms into the RFID system's control logic enhances its ability to respond intelligently to changes in the operating environment, contributing to improved performance and reliability in long-range RFID applications.

This paper introduces the design of a new frequency-reconfigurable ultra-high frequency radio frequency identification (UHF RFID) antenna, demonstrating an innovative approach that enables dynamic adjustment of its resonance frequency. The proposed antenna design features a central dipole structure, enhanced by two hexagonal split-ring resonators (H-SRR) at each end. A T-match network is integrated into the center of the dipole, which is essential for achieving impedance matching between the antenna and the Alien Gen2 H4 RFID microchip. The antenna is designed using a Rogers 4350B substrate, a high-performance dielectric material ideal for RFID applications. With dimensions of 68×32.6×1.524 mm3, the compact antenna maintains full UHF band (860 MHz to 930 MHz) coverage compliant with International Telecommunications Union (ITU) RFID standards. This ensures that the antenna can be used in different regions around the world, offering broad compatibility with various RFID systems. The antenna's frequency reconfigurability is achieved through the integration of localized capacitors with variable values, which plays a key role in enabling precise adjustments to the antenna's center frequency across the entire UHF band. Extensive simulation results validate the effectiveness of this reconfigurable design, demonstrating that the antenna can dynamically adjust its frequency while maintaining excellent performance metrics, including impedance matching, radiation efficiency, and bandwidth. This makes the proposed antenna an ideal choice for modern RFID applications.

This article presents the design and implementation of an internet of things (IoT)-based weather forecasting system aimed at optimizing operational planning for renewable energy generation. The system leverages a Raspberry Pi as its central controller, integrating pyranometer and anemometer sensors for real-time data collection and predictive analytics. Utilizing the double moving average method, the system provides accurate short-term forecasts of solar and wind power outputs, which are crucial for addressing the intermittency challenges of renewable energy sources. The integration with the Blynk platform ensures user-friendly data visualization and accessibility. Results from a three-day testing phase reveal the system's high accuracy, with prediction errors of 8.79% for solar power and 16.49% for wind power. These findings underscore the system's potential to enhance energy planning, improve efficiency, and support sustainability goals. By enabling data-driven decision-making, this IoT-based forecasting system offers a scalable solution for advancing renewable energy integration into the power grid.

Organizations worldwide commonly utilize cloud infrastructure to manage large volumes of data, making the optimization of storage crucial for enhancing cloud performance. One effective optimization technique is data deduplication, which identifies duplicate objects and ensures that only one copy of unique data is stored in the cloud. While several deduplication schemes currently exist, there is a pressing need to improve efficiency in cloud storage through innovative approaches. In this paper, we propose a new system model designed to facilitate an efficient deduplication process. Our algorithm, called deduplication in cloud infrastructure (DCI), offers a systematic and effective method for handling deduplication challenges related to redundant data storage. DCI focuses on hash generation, metadata comparison, and pointer-based deduplication, providing a comprehensive strategy for optimizing cloud storage resources and minimizing duplication. This ultimately enhances both the efficiency and cost-effectiveness of cloud-based data management. A simulation study using CloudSim and the Hadoop distributed file system (HDFS) simulator demonstrates that the proposed deduplication method is effective. Experimental results show that our algorithm outperforms many existing solutions, achieving the highest deduplication ratio of 6.7 and saving 85.09% of storage space due to its efficient deduplication approach. The proposed system can be used in cloud infrastructures for efficiency.

Hepatocellular carcinoma (HCC), the primary cancer of the liver, is life-threatening, with few or no symptoms, and detection in the early stage will help for successful treatment with surgery, and transplant for a better life quality. Here, we proposed two stacking classification models based on deep learning with differential hybrid feature selection for the early detection of HCC using novel non-invasive biomarker PIVKA-II. We showed how the variations in hybrid feature selection affect the performance of stacking classification and different supervised machine-learning algorithms on a metaclassifier. The base layers were support vector machine (SVM), gradient boosting (GB), and linear discriminant analysis (LDA). The meta classifier was a multilayer perceptron (MLP) with three different optimizers, stochastic gradient descent (SGD), adaptive moment estimation (ADAM), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Our first model outperformed the second with their hybrid features by improving accuracy by 1.5% and F1_score by 1% in both SGD and ADAM optimization, while MLP-LBFGS had a 1.4% increase in accuracy. The precision had hiked by 1.9%, 3.5%, and 1.7% in SGD, ADAM, and LBFGS, respectively, in Model-1. Matthew’s correlation coefficient (MCC) for MLP-SGD increased from 0.82 to 0.85, MLP-ADAM from 0.81 to 0.85, and MLP-LBFGS from 0.75 to 78 for the first model.

Water pollution, particularly from floating debris such as plastics, has become a critical environmental issue, threatening aquatic ecosystems and biodiversity. Autonomous solutions for the detection and removal of waste are increasingly essential for maintaining water cleanliness and mitigating pollution. However, existing systems face limitations in real-time detection, accuracy, and adaptability to diverse aquatic environments. This paper utilizes the water pollution images dataset, comprising almost 300 high-resolution images from lakes, rivers, and coastal areas, representing various types of floating waste under different environmental conditions. In response to these challenges, this paper introduces an autonomous unmanned surface vehicle (USV) system equipped with the enhanced waste detection network (EWD-Net). EWD-Net improves upon traditional single-shot detection algorithms by integrating deeper feature extraction layers and enhancing computational efficiency, resulting in higher accuracy and faster detection. Additionally, the system includes the dynamic path optimization (DPO) module for efficient navigation and obstacle avoidance in complex water environments. The novelty of this system lies in its dual approach, combining advanced detection with optimized path planning, ensuring effective autonomous operation. The results indicate that the proposed model achieves an accuracy of 94.6%, outperforming existing algorithms and providing a robust solution for real-time waste detection and collection.

This paper presents a thorough examination of two prominent speech-to-text translation (STT) models: the end-to-end (E2E) model and the cascade model. STT is a critical technology in today’s multilingual society, facilitating communication across language barriers. The study focuses on comparing these models using a multicriteria approach to evaluate their effectiveness in translating speech to text. The E2E model represents a unified architecture that directly translates speech into text, while the cascade model involves separate modules for speech recognition and machine translation (MT). Both models have distinct advantages and challenges, which are explored in detail. Through a multicriteria comparison, this research assesses various performance metrics and criteria to determine the strengths and weaknesses of each model. The weighted sum method is employed to assign weights to evaluation criteria, providing a systematic evaluation framework. The findings have implications for researchers and developers in STT. By understanding the comparative performance of E2E and cascade models, researchers can make informed decisions regarding model selection based on criteria such as accuracy, speed, robustness, and resource requirements. This research advances the understanding of speech translation technologies and provides a foundation for future studies to refine evaluation methodologies, explore hybrid models, and enhance translation quality.

Texture analysis is a fundamental approach in image processing for identifying specific patterns or structures. One widely used method is the grey-level co-occurrence matrix (GLCM), which computes the frequency of pixel value pairs at certain distances and angles. This study adapts the GLCM method for 1D electroencephalogram (EEG) signal analysis, focusing on extracting features such as contrast, energy, homogeneity, correlation, and entropy. EEG signals are normalized to the range 0–255, and the extracted features are classified using a support vector machine (SVM). Experimental results show that combining features across multiple distances (d=1 to 20) achieves classification accuracy of 78.8% for five classes (Z/O/N/F/S), 94.0% for three classes (O/F/S), and 94.3% for another three-class group (Z/N/F). The method shows strong performance for short to medium distances and fewer class combinations. However, performance declines when dealing with more complex class sets and longer distances, where texture features become less effective. The drop in accuracy for Z/O/N/F/S beyond d=5 underscores the challenges of maintaining feature robustness at extended distances. Despite this, GLCM remains a promising approach for EEG signal classification. Future work should focus on optimizing distance parameters and feature combinations to further enhance classification performance.

Time series forecasting with cyclicality is key to the development of green energy, particularly wind energy, due to its high volatility. Accurate forecasting allows for optimal use of energy storage systems and balancing of power grids. In this article, the authors have developed a model for forecasting time series in wind energy through the combined use of Fourier transform and an adapted transformer architecture to solve the time series forecasting problem. The use of Fourier transform provided the ability to detect and account for hidden periodicities that may not be obvious in simple time series analysis, and allowed for the separation of random fluctuations from significant cyclical components, contributing to more accurate data analysis. The use of transformer architecture made it possible to effectively account for both short-term fluctuations and long-term trends in wind patterns, creating more accurate and reliable forecasts of wind energy production. The results show that the model outperforms methods such as transformers, long short term memory (LSTM), LSTM with Fourier transform, and DeepAR in forecast accuracy, taking into account seasonal, weather, and daily cycles of wind data.

The internet of things (IoT) comes with great capabilities as well as new opportunities for attackers and criminals. Even though digital forensics is considered to be mature and has been studied by many researchers in recent years, IoT forensics is relatively new and yet to be thoroughly explored. IoT technology has unique characteristics that require adoption of the traditional digital forensic approaches, frameworks, and tools. The primary goal of IoT forensics is to collect, preserve, and present evidence in a manner that meets legal standards and country law from a specific IoT system that includes connected devices and sensors via different types of networks and associated cloud environments. In this paper, we explored the main difference between IoT forensics and traditional digital forensics. This paper aims to provide a comprehensive and up-to-date overview of recently proposed solutions for addressing the IoT forensic domain. we highlighted the limitations of the recently proposed framework and the utilized technologies by researchers. In addition, we recommended some new research directions that could enhance the IoT investigation process. The goal of this paper is to provide a clear understanding of the currently used technologies and other fields in IoT forensic frameworks, limitations, and directions in this area, which may be helpful for future researchers interested in this field.