New Concept of PLC Modems: Multi-Carrier System for Frequency Selective Slow-Fading Channels Based on Layered SCCC Turbocodes

The article introduces a novel concept of a PLC modem as a complement to the existing G3 and PRIME standards for communications using medium- or high-voltage overhead or cable lines. The proposed concept is based on the fact that the levels of impulse noise and frequency selectivity are lower on high-voltage lines than on low-voltage ones. Also, the demands for “cost-effective” circuitry design are not so crucial as in the case of modems for low-voltage level. In contract to these positive conditions, however, there is the need to overcome much longer distances and to take into account low SNR on the receiving side. With respect to the listed reasons, our concept makes use of MCM, instead of OFDM. The assumption of low SNR is compensated through the use of an efficient channel coding based on a serially concatenated turbo code. In addition, MCM offers lower latency and PAPR compared to OFDM. Therefore, when using MCM, it is possible to excite the line with higher power. The proposed concept has been verified during experimental transmission of testing data over a real, 5 km long, 22kV overhead line

Massive development of online learning materials

We participated in collaborative development of more than six hundred comprehensive online teaching materials for higher professional schools in the Czech Republic. The paper focuses on explaining the procedures and methods that we used to make the development process as efficient as possible, which was necessary with respect to the quantity of the materials and the limited time. Managing the logistics was the key, as several hundred people took part in the project. We are also describing, from the edagogical point of view, the OER structure, its division into educational blocks, and types of educational objects

Conceptual Design Tool for Fuel-Cell Powered Micro Air Vehicles

A conceptual design tool was built to explore power requirements of a hybrid-power system for Micro Air Vehicles (MAVs) comparable in size to the Cooper\u27s Hawk. An inviscid aerodynamic code, Athena Vortex Lattice (AVL), and a motor-propeller analysis code, QPROP, provide overall lift, drag, and thrust data for power-required calculation as functions of many variables to include mass, platform geometry, altitude, velocity, and mission duration. Phoenix Technologies’ Model Center was used to integrate multi-disciplinary components that employ specific power and specific energy of two power sources to determine power system mass required for a designated mission. The tool simulated a mission for the fixed wing Generic Micro Aerial Vehicle (GenMAV), and relative sizing between a high specific power source and a high specific energy source was investigated. Current small fuel cell technology provides inadequate specific power. It was found that a MAV-sized fuel cell-battery hybrid-power system would not perform better than a pure battery or battery-battery power system. A feasible fuel cell capability requirement of at least 325 W/kg matched with at least 921 W-hr/kg was identified as a fuel cell – Li-Po solution for a defined 30 min mission resulting in reduced power system mass compared to using only Li-Po batteries

Differentially Private Fair Binary Classifications

In this work, we investigate binary classification under the constraints of both differential privacy and fairness. We first propose an algorithm based on the decoupling technique for learning a classifier with only fairness guarantee. This algorithm takes in classifiers trained on different demographic groups and generates a single classifier satisfying statistical parity. We then refine this algorithm to incorporate differential privacy. The performance of the final algorithm is rigorously examined in terms of privacy, fairness, and utility guarantees. Empirical evaluations conducted on the Adult and Credit Card datasets illustrate that our algorithm outperforms the state-of-the-art in terms of fairness guarantees, while maintaining the same level of privacy and utility

CLASSIFICATION ALGORITHMS WITH DIFFERENTIAL PRIVACY AND FAIRNESS GUARANTEES

Machine learning algorithms are increasingly used in high-stakes decision-making tasks, highlighting the need to evaluate their trustworthiness, especially regarding privacy and fairness. Models must protect individual privacy and avoid discriminating against demographic subgroups. Differential Privacy (DP) has become the standard for privacy-preserving machine learning. It is generally divided into central DP, which relies on a trusted curator, and local DP (LDP), where no trusted entity is assumed. The first part of this thesis investigates binary classification under the constraints of both central DP and fairness. We propose an algorithm based on the decoupling technique to learn a classifier that guarantees fairness. This algorithm takes classifiers trained on different demographic groups and produces a single classifier satisfying statistical parity. We then refine this algorithm to incorporate DP. The performance of the resulting algorithm is rigorously analyzed in terms of privacy, fairness, and utility guarantees. Empirical evaluations on the Adult and Credit Card datasets show that our algorithm outperforms state-of-the-art methods in fairness while maintaining the same levels of privacy and utility. The second part of this thesis addresses the design of an optimal pre-processing method based on LDP mechanisms to minimize data unfairness and reduce classification unfairness. For binary sensitive attributes, we derive a closed-form expression for the ``optimal'' mechanism. For non-binary sensitive attributes, we formulate an optimization problem that, when solved algorithmically, yields the optimal mechanism. We theoretically prove that applying these pre-processing mechanisms leads to lower classification unfairness using the notion of discrimination-accuracy optimal classifiers. Empirical evaluations on multiple datasets demonstrate the effectiveness of these mechanisms in reducing classification unfairness, highlighting LDP’s potential as a tool for enhancing fairness. This contrasts with central DP, which has been shown to adversely affect fairness.ThesisMaster of Science (MSc)Fairness and privacy are two key concepts in trustworthy machine learning. In high-stakes scenarios, models must protect individual privacy while avoiding discrimination against demographic subgroups. Differential privacy (DP), the standard notion of privacy in machine learning these days, is divided into two main categories: central DP and local DP (LDP). The first part of this thesis examines the interplay between central DP and fairness in binary classification, presenting an algorithm that guarantees both privacy and fairness while providing theoretical performance guarantees. This algorithm is evaluated on real-world datasets, showing improved fairness without compromising privacy or utility. The second part introduces an optimal data pre-processing method using LDP to minimize unfairness, demonstrating an application of LDP in reducing unfairness in model predictions. Experiments on various datasets show that this optimal pre-processing outperforms existing LDP-based pre-processing fairness intervention methods and state-of-the-art fairness post-processing, achieving better fairness while maintaining comparable utility, even when compared to non-private scenarios

New tools to motivate STEM students towards early-career self-management

The Universitat Politècnica de Catalunya (UPC), the Czech Technical University in Prague (CTU) and the Universidade de Lisboa have recently undertaken the Engine4STEMers project, a joint initiative devoted to generating a new culture of young STEM graduates willing to assume early-career responsibilities, oriented to satisfy societal challenges. It also aims to provide guidance and motivation to help young graduates adopt early-career leadership and managerial skills. In this context, this short article describes the objectives and content of a short pilot seminar that is currently taking place at UPC and that is dedicated to the concept of service, a central educational tool of Engine4STEMers that aims to motivate students to proactively manage a successful transition from university to the labour market