The ability of electronic devices to act as switches makes digital information processing possible. The current silicon-based semiconductor processors are fabricated according to a top-down principle. However, the need to scale down in the size of such electronic devices has prompted the search for molecule-based information processing components (Molecular Electronics), such as switching memories, sensors and logic gates. Concretely, within the past two decades, developments in Nanotechnology have shown the capabilities of molecules to perform some of the computational logic functions - relating to the concept of logical zeros (0) and ones (1) binary code - achieved in mainstream semiconductor technology. Molecular logic gates differ from the currently used semiconductor elements by small size, multifunctional nature and variability of input and output signals. Nonetheless, the transition of logic elements from mostly optical means for reading output signals to electronic transduction tools would be beneficial for developing many novel logic elements for information processing, (bio)sensing and actuation. Accordingly, the design, construction and miniaturization of molecular electronic systems capable of performing complex logic functions is a current challenge. Herein, 3D printing technology is presented as a promising tool to open up new horizons in the field of electronic devices in general, and molecular logic gates in particular. For this goal, a sustainable bottom-up approach has been devised for the development of the next generation of “intelligent” 3D-printed electronic devices - 3D-printed responsive interfaces -, where bistable (supra)molecular switches will be electrically read out on carbon-based 3D-printed conductive substrates as the proof. Accordingly, R3DINBOW is in strong agreement with the EU’s digital strategy, while helping to achieve its target of a climate-neutral Europe by 2050 and responding to the current needs of our Society.

This project focuses on automatic speaker recognition (SID), the task of determining the identity of the speaker in a speech recording. Disentangling the speaker specific information from the rest of nuisance variability requires complex models. Deep neural networks (DNNs) have recently showed their potential for this, as the popular x-vector learnt by a DNN. Here, we aim for end-to-end SID where the system is optimized as a whole for the target task. Despite several attempts in this line of research, many aspects still remain unexplored or not explored thoroughly. We also propose to explore recurrent approaches, suitable for dealing with temporal signals, as well as different pooling methods to obtain a fixed-length representation from a variable length input sequence of speech features. Next, we want to explore different flavors of attention mechanisms, which make the DNN to focus on relevant parts of the input, providing a way to quantify how much evidence has been collected about the speaker identity and the uncertainty of the obtained representation, which is a critical issue when making (Bayesian) decisions in SID. Finally, some other approaches such as using the raw signal (instead of features) or other advances that might arise will be also explored for SID and related tasks. To achieve our goals, we will start from theory, implement the proposed approaches and test on public SID benchmarks such as NIST SREs. The outcomes are intended to benefit both scientific community and speech processing industry. The applicant Dr. Alicia Lozano-Diez is an excellent female researcher, who has done her Ph.D. at Audias (Universidad Autonoma de Madrid, Spain), a respected research lab. The host group Speech@FIT from Brno University of Technology (Czechia) has a top-class track on speech processing research. Thus, we expect the combination of both the researcher and the host to boost the researcher career and benefit the host group (and its industrial European partners).

The proposed project deals with Speaker Diarization (SD) which is commonly defined as the task of answering the question “who spoke when?” in a speech recording. The first objective of the proposal is to optimize the Bayesian approach to SD, which has shown to be promising for the tasks. For Variational Bayes (VB) inference, that is very sensitive to initialization, we will develop new fast ways of obtaining a good starting point. We will also explore alternative inference methods, such as collapsed VB or collapsed Gibbs Sampling, and investigate into alternative priors similar to those introduced for Bayesian speaker recognition models. The second part of the proposal is motivated by the huge performance gains that, in recent years, have been brought to other recognition tasks by Deep Neural Networks (DNNs). In the context of SD, DNNs have been used in the computation of i-vectors, but their potential was never explored for other stages of SD. We will study ways of integrating DNNs in the different stages of SD systems. The objectives of the proposal will be achieved by theoretical work, implementation, and careful testing on real speech data. The outcomes of the project are intended not only for scientific publications, but eagerly awaited by European speech data mining industry (for example Czech Phonexia or Spanish Agnitio). The project is proposed by an excellent female researcher, Dr. Mireia Diez, having finished her thesis in the GTTS group of University of the Basque Country, one of the most important European labs dealing with speaker recognition and diarization. The proposed host is the Speech@FIT group of Brno University of Technology, with a 20-year track of top speech data mining research. The proposed research training and combination of skills of Dr. Diez and the host institution have chances to advance the state-of-the-art in speaker diarization, provide the applicant with improved career opportunities and benefit European industry.

Two-dimensional (2D) Janus transition metal dichalcogenides (Janus 2D TMD) are specific planar materials with different top and bottom termination layers. This new class of materials thus exhibits broken structural symmetry in the out-of-plane direction, predicted to result in exotic new properties, such as vertical piezoelectricity, Rashba spin splitting and unexpected exciton behavior. However, fabrication of Janus 2D TMD is an extremely challenging task, owing to the need of an epitaxial atomic replacement within classical 2D TMD. This project is designed to achieve chalcogenide exchange within classical TMDs by two novel approaches. In the first approach, we will utilize an electron beam as tool to alter the top layer, allowing its selective selenization/sulfurization. In the second approach, we aim to utilize increased reactivity in the van der Waals gap to allow for selective chalcogenide exchange within the gap. This requires careful set of experimental conditions. These will be sought with in-situ electron microscopy, which allows to overcome the common experimental "black box" approach. Data from in-situ microscopy allow to determine kinetic constants, potentially allowing to generate general growth protocols for Janus TMDs. The growth will be followed analysis of structural, optical and electrical properties of the grown materials. The proposal builds on an established know how of the Host institution (CEITEC) and the Supervisor (Miroslav Kolibal), providing the Aplicant (Estácio Paiva de Araújo) with relevant scientific training, as well as widening his skillset via Career Development Plan. The project emphasizes the two-way knowledge transfer; the Applicant is expected to bring both the scientific know-how, as well as to open new ways to communicate the results to the different audiences and, as such, increase the visibility of CEITEC within the scientific community and general public.

Portable and wearable devices including smartwatches, health monitoring, and multimedia devices are becoming increasingly popular in our daily lives. These devices are generally powered by batteries that have a limited lifetime. Recently, the development of triboelectric nanogenerators (TENGs) has shown to be an effective approach to transforming biomechanical energy to power up these devices. However, TENGs generate low energy and AC signals which limit their use in continuously powering up electronics. The AC signals of TENGs must be converted and stored in energy storage. Among energy storage devices, supercapacitors (SCs) are found to be a promising device due to their high power density, moderate energy density, long cycle life, and safe use. Hence, this project aims to develop an integrated device (TENGSC), connecting a high-performance TENG with an SC, which can store the transformed biomechanical energy. However, the TENG and SC are susceptible to undergoing damage during biomechanical actions. This mechanical damage can be overcome by developing self-healable TENG and SC. The self-healing nature will help to restore their properties if any damage happens during the cyclic movements. Moreover, to harvest high power from the TENG, a 3D printing technique will be followed, which can easily introduce micropatterns on the film surface. The micro-patterns provide higher frictional effect which is the key factor in increasing the conversion efficiency of TENG. Besides, the energy density of the SC can be increased through using porous MXenes –Ti3C2 as electrode materials. This can be developed through the 3D printing of a Ti3C2/graphite–based polyethylene terephthalate (PET) filament followed by pyrolysis. The waste drinking water bottles can be used as PET source. Thus, through this work, biomechanically driven smart power source will be developed along with concept of waste to wealth transformation, which can be used in portable and wearable electronics.