Search

While text-based CAPTCHAs have been the predominant type of human interaction proofs (HIPs) for many years, image recognition challenges have also gained significant attention. This trend is due, on one hand, to groundbreaking advancements in solving text CAPTCHAs and, on the other hand, to the intrinsic weakness of machines in dealing with cognitive tasks such as those presented in image CAPTCHAs. In addition to classic and even human-centric image CAPTCHA solvers, deep learning has recently emerged as a significant player, providing two unprecedented and, at the same time, contradictory advantages for designers and adversaries. Designers benefit from deep learning techniques to make CAPTCHAs as hard to break as possible, while adversaries utilize deep learning algorithms to attack novel and complicated image-based challenges. Given these premises, this paper presents an analytical study on the applications of deep learning for and against image CAPTCHAs. This study aims to provide a comprehensive overview of the latest advancements in the field, assisting researchers and practitioners in designing image CAPTCHAs that are both user-friendly and resilient against modern attacks.

Abstract Volcano-seismic signals can help for volcanic hazard estimation and eruption forecasting. However, the underlying mechanism for their low frequency components is still a matter of debate. Here, we show signatures of dynamic strain records from Distributed Acoustic Sensing in the low frequencies of volcanic signals at Vulcano Island, Italy. Signs of unrest have been observed since September 2021, with CO2 degassing and occurrence of long period and very long period events. We interrogated a fiber-optic telecommunication cable on-shore and off-shore linking Vulcano Island to Sicily. We explore various approaches to automatically detect seismo-volcanic events both adapting conventional algorithms and using machine learning techniques. During one month of acquisition, we found 1488 events with a great variety of waveforms composed of two main frequency bands (from 0.1 to 0.2 Hz and from 3 to 5 Hz) with various relative amplitudes. On the basis of spectral signature and family classification, we propose a model in which gas accumulates in the hydrothermal system and is released through a series of resonating fractures until the surface. Our findings demonstrate that fiber optic telecom cables in association with cutting-edge machine learning algorithms contribute to a better understanding and monitoring of volcanic hydrothermal systems.

The recent increasing demand of Silicon-on-Chip devices has triggered a significant impact on the industrial processes of leading semiconductor companies. The semiconductor industry is redesigning internal technology processes trying to optimize costs and production yield. To achieve this target a key role is played by the intelligent early wafer defects identification task. The Electrical Wafer Sorting (EWS) stage allows an efficient wafer defects analysis by processing the visual map associated to the wafer. The goal of this contribution is to provide an effective solution to perform automatic evaluation of the EWS defect maps. The proposed solution leverages recent approaches of deep learning both supervised and unsupervised to perform a robust EWS defect patterns classification in different device technologies including Silicon and Silicon Carbide. This method embeds an end-to-end pipeline for supervised EWS defect patterns classification including a hierarchical unsupervised system to assess novel defects in the production line. The implemented “Unsupervised Learning Block” embeds ad-hoc designed Dimensionality Reduction combined with Clustering and a Metrics-driven Classification Sub-Systems. The proposed “Supervised Learning Block” includes a Convolutional Neural Network trained to perform a supervised classification of the Wafer Defect Maps (WDMs). The proposed system has been evaluated on several datasets, showing effective performance in the classification of the defect patterns (average accuracy about 97%).

Soft switching for both primary and secondary side devices is available by using LLC converters. This resonant converter is an ideal candidate for today’s high frequency, high efficiency, and high power density applications like adapters, Uninterrupted Power Supplies (UPS), Solid State Transformers (SST), electric vehicle battery chargers, renewable energy systems, servers, and telecom systems. Using Gallium-Nitride (GaN)-based power switches in this converter merits more and more switching frequency, power density, and efficiency. Therefore, the present paper focused on GaN-based LLC resonant converters. The converter structure, operation regions, design steps, and drive system are described precisely. Then its losses are discussed, and the magnets and inductance characteristics are investigated. After that, various interleaved topologies, as a solution to improve power density and decrease current ripples, have been discussed. Also, some challenges and concerns related to GaN-based LLC converters have been reviewed. Commercially available power transistors based on various technologies, i.e., GaN HEMT, Silicon (Si) MOSFET, and Silicon Carbide (SiC) have been compared. Finally, the LLC resonant converter has been simulated by taking advantage of LTspice and GaN HEMT merits, as compared with Si MOSFETs.

The use of a resistive shunt is one of the simplest and most used methods for measuring current in an electronic device. Many researchers use this method to measure drain current during short-circuiting of fast devices such as GaN HEMTs. However, the high switching speed of these devices together with the non-ideality of the shunt resistors produces an overestimation of the current in the initial phases of the transient. In this paper, a passive compensation network is proposed, which is formed by adding an inductor to the voltage measurement circuit and allows an accurate measurement of the current using the resistive shunt even in the presence of very fast devices. The proposed method is validated by simulations and experimental measurements.