Wireless Power Transfer (WPT) is a promising technique, yet still an experimental solution, to replace batteries in existing implants and overcome the related health complications. However, not all techniques are adequate to meet the safety requirements of medical implants for patients. Ensuring a compromise between a small form factor and a high Power Transfer Efficiency (PTE) for transcutaneous applications still remains a challenge. In this work, we have used a resonant inductive coupling for WPT and a coil geometry optimization approach to address constraints related to maintaining a small form factor and the efficiency of power transfer. Thus, we propose a WPT system for medical implants operating at 13.56 MHz using high-efficiency Complementary Metal Oxide-Semiconductor (CMOS) components and an optimized Printed Circuit Coil (PCC). It is divided into two main circuits, a transmitter circuit located outside the human body and a receiver circuit implanted inside the body. The transmitter circuit was designed with an oscillator, driver and a Class-E power amplifier. Experimental results acquired in the air medium show that the proposed system reaches a power transfer efficiency of 75.1% for 0.5 cm and reaches 5 cm as a maximum transfer distance for 10.67% of the efficiency, all of which holds promise for implementing WPT for medical implants that don’t require further medical intervention, and without taking up a lot of space.

High-dimensional and incomplete (HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis (LFA) model is capable of conducting efficient representation learning to an HDI matrix, whose hyper-parameter adaptation can be implemented through a particle swarm optimizer (PSO) to meet scalable requirements. However, conventional PSO is limited by its premature issues, which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer (SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency. Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

Grid-connected inverters (GCI) are commonly used in PV system applications to generate a regulated AC current to feed into the grid. Transformerless inverters are the most advanced inverters that are used in industry, which provide efficiency with smaller size and lower cost. This paper proposes a grid-connected single-phase transformerless inverter with the technology of common ground and the virtual DC bus concept. In this topology, the grid neutral is connected directly to the PV ground, which generates a constant common mode voltage (CMV), thus leading to the elimination of the leakage current caused by the PV array’s parasitic capacitance. The proposed inverter has a buck–boost circuit with a flying capacitor to generate the DC bus for a negative power cycle, four switches, and two diodes. A unipolar sinusoidal pulse width modulation (SPWM) technique is used which reduces the output filter requirements. In addition, only one switch carries the load current during the active states of both the negative and positive power cycle, thus minimizing the conduction losses. One more advantage presented in the proposed inverter is its ability to charge the flying capacitor during all operation states due to the existence of the buck–boost circuit. Design and theoretical calculations were conducted in this paper to optimize the losses. Moreover, the PSIM simulation was used to validate the proposed topology inverter, verify the performance by showing leakage current elimination, and achieve unipolar voltage in the output bus. The simulation results show a peak efficiency of 98.57% for a 2 kW inverter, which agrees with the theoretical calculations.

The detection technology for metal surface defects plays a crucial role in improving metal product quality and production efficiency in various manufacturing and 3-D printing factories. Metal defect detection faces scale variation and irregular shapes, which limit the adaptability of general object detection models in industrial scenarios. Deep learning-based methods are widely used for metal surface defect detection due to their strong adaptability and high automation. Yet, their existing studies pay limited attention to adaptability, evaluation, and recommendations across different detection methods for metal surface defects. This work mainly discusses YOLO, R-CNN, and transformers, as well as FPN, and analyzes their applications in metal surface defect detection according to their respective characteristics, to provide guidance for future research. YOLO has advantages in real-time industrial online detection, while R-CNN and transformer models show potential advantages in handling complex defect cases. Additionally, this work summarizes commonly used datasets and evaluation metrics for metal surface defect detection and analyzes the benchmark performance of different types of detection methods. It also discusses future research directions, including the current status and improvement paths of different models in terms of accuracy, real-time performance, and adaptability. Future models should focus on balancing accuracy and real-time performance, exploring new hybrid architectures, and improving adaptability to different metal surface defects to support further development in this field.