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This work presents an application of the Perceptually Important Points (PIP) technique for the analysis of VLF time series. The aim of the analysis is to detect anomalies with respect to the normal variations of the data trends. Such anomalies could reveal possible radio precursors of the earthquake. Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10–50 kHz) and LF (150–300 kHz) radio precursors of earthquakes. The time series used for experiments was collected during the Dodecanese islands earthquakes ( $\text{MW}=5.6$ and $\text{MW}=5.7$ ) occurred on January 30, 2020.

This paper presents a Track and Hold sampling buffer topology, which allows sampling the signal inside the buffer itself while achieving very high linearity. The circuit operations and its large-signal behavior are analyzed and the key design strategies to maximize linearity are discussed. Then, a 60 GS/s, 52.6 dB SFDR, 8 ways interleaved simulated prototype in TSMC 5 nm technology, consuming 2. 52 mW from a 0.9 V supply, is compared to the state-of- the-art sampling buffers, showing linearity improvement.

The article of record as published may be found at http://dx.doi.org/10.1109/SPW53761.2021.00048 2021 IEEE Symposium on Security and Privacy Workshops Modern web applications rely heavily on dynamic content, i.e., page updates made by the browser using an XMLHttpRequest and more recently the JavaScript Fetch API. These requests are often made on behalf of user actions, such as typing on the keyboard or pointing at an HTML element. As a result, the timings of the user's actions are strongly correlated with the timings of packets that carry these events. In this work, we examine several dynamic web applications and the ability to measure human behavior in encrypted network traffic by using deep temporal features. Our approach relies on the ability to accurately detect a subset of packets that correspond to user actions. Leveraging recent work in keystroke dynamics, we show that user identification can be performed with modest accuracy utilizing the packet timings induced by a user typing into a search engine. While this tool could be used by forensic investigators to perform target identification among encrypted network traffic, it also raises a privacy concern in which an on-path remote adversary able to detect these packets may infer user behaviors. Work done while a student at the Naval Postgraduate School

This paper presents a patient-centered interaction project of Pepper humanoid robotic therapy applications for children with attention deficit. This new therapeutic methodology was created to support and make therapeutic work more attractive. Pepper comes with a tablet and two identical cameras. The tablet is used to make the patients interact with the application, while the cameras are used to capture their emotions in real-time to understand the degree of attention and any difficulties they may have. The interaction with the tablet takes place through some exercises in the form of games. The exercises performed by the patients are analyzed and combined with the data acquired by the cameras. The combination of these data is elaborated to propose adequate levels of therapeutic activity. This process leads to the digitization of the patients' therapeutic path so that any improvement (or worsening) is monitored and makes Pepper a reliable and predictable technological intermediary for the child. The work was developed in collaboration with a diagnostic and therapeutic center, where it is being tested. By interacting with a humanoid robot, children show greater involvement, which can be explained, according to psychologists, by the fact that a robot is emotionally less rich than humans and the patients feel less fearful.

This paper proposes a design methodology for capacitor-based equalization circuits that takes into account relevant characteristics of both battery pack and power electronics components. An analytical models have been developed for a switched capacitor (SC) architecture considering ideal and real conditions. The models developed are then used for defining a design methodology that allows for sizing the SC architecture according to the desired performances in terms of efficiency and mean balancing current. It is also noted that the proposed design methodology can be adopted to other capacitor-based equalizers. A prototype of the SC architecture for six series-connected cells has been developed as well in order to evaluate the correct functionality of the equalization circuit and to validate the proposed design methodology.

Uncontrolled wildfires are dangerous events capable of harming people safety. To contrast their increasing impact in recent years, a key task is an accurate detection of the affected areas and their damage assessment from satellite images. Current state-of-the-art solutions address such problem through a double convolutional neural network able to automatically detect wildfires in satellite acquisitions and associate a damage index from a defined scale. However, such deep-learning model performance is strongly dependent on many factors. In this work, we specifically focus on a key parameter, i.e., the loss function, exploited in the underlying neural networks. Besides the state-of-the-art solutions based on the Dice-MSE, among the many loss functions proposed in literature, we focus on the Binary Cross-Entropy (BCE) and the Intersection over Union (IoU), as two representatives of the distribution-based and region-based categories, respectively. Experiments show that the BCE loss function coupled with a double-step U-Net architecture provides better results than current state-of-the-art solutions on a public labeled dataset of European wildfires.

Insider threats are a costly and dangerous problem for government and non-government organizations alike. Considering an insider’s inherently privileged level of access on a network, the main principle of network defense-keep potential threats and outsiders out-does not apply to insider threats. Current defenses are largely based on the detection of insider threat indicators which are often manually compiled from past events. This approach is limited in scalability, has difficulty generalizing to new threats, and fails to consider the wide range of behaviors within an organization. In this work, we describe a system that detects potential insider threats through the characterization of temporal behavior on a network. Our approach is completely unsupervised, based on the assumption that there are many different behavioral norms within a network. After testing the system on an operational network with over 8,000 hosts, we show through a series of case studies that the approach is effective in detecting behavioral anomalies suitable for follow up by a human analyst.

This paper deals with a new adaptive balancing control algorithm for passive and distributed Battery Management System (BMS) for automotive applications. A calibrated first-order model for the battery cells and thermal model of the BMS slaves unit have been considered in the development of the proposed control. It performs the balancing process of the cells by managing the temperature of the BMS slave unit. Taking into account the thermal constraints of the system, the proposed control dynamically selects the number of the cells to be balanced for each battery module and the optimal temperature of the BMS slave units in order to minimize the equalization time. A deep numerical analysis is presented to validate the performance of the proposed approach.

Reliable and autonomous monitoring systems have been increasingly considered to enhance composite based aerostructures. Unforeseen events like low velocity impacts induce barely visible damage affecting the structure capability to withstand expected loads. Using several transducers capable to excite and detect guided waves, it is possible to efficiently interrogate the structure and detect, locate and characterize typical damage scenarios. However, the on-line detection of small emerging flaws requires instruments enabling measurements on multiple channels with extremely high electronic capabilities. Such devices are requested to simultaneously sort sensors for pitch-catch analysis with one interrogating transducer. Furthermore an integrated software is necessary to elaborate data and provide the diagnosis. This article introduces the design of a compact solution together with an active approach for structural inspection. The detailed description of the device functionality as well as the description of the localization system are discussed. Finally, the evaluation of the overall system capabilities is investigated on a carbon fiber reinforced plastic (CFRP) plate which is designed for a real scale aircraft wing demonstrator. The results summarize the efficient solution in terms of feasible device and reliable localization approach.