Archivio della ricerca - Fondazione Bruno Kessler
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Linearly multiplexed Photon Number Resolving single-photon detectors array
No full textPhoton Number Resolving Detectors (PNRDs) are devices capable of measuring the number of photons present in an incident optical beam, enabling light sources to be measured and characterized at the quantum level. In this paper, we explore the performance and design considerations of a linearly multiplexed photon number-resolving single-photon detector array, integrated on a single mode waveguide. Our investigation focus on defining and analyzing the fidelity of such an array under various conditions and proposing practical designs for its implementation. Through theoretical analysis and numerical simulations, we show how propagation losses and dark counts may have a strong impact on the performance of the system and highlight the importance of mitigating these effects in practical implementations
RF MEMS switch optimization using ANN and design of antenna with frequency reconfigurability
No full textArtificial neural networks (ANN) are becoming highly prominent in the optimization of micro-RF devices, which are very significant in wireless communication applications. In this manuscript, we present the optimization of RF MEMS switches using ANN and the design of an antenna with frequency reconfigurability. A unique procedure is proposed to design reconfigurable antennas with RF MEMS switches using ANN. The novelty of this work lies in the creation of a dedicated dataset for the considered RF MEMS switch with FEM tool simulation and the utilization of cascade feed-forward neural networks for optimization. The design of the dataset and the optimization of RF MEMS switches in different aspects using ANN are the key contributions of this work. Comprehensive analysis was performed using a neural network with the designed dataset. Cascade feed-forward neural networks are highly efficient when compared with other neural networks. The weights and biases of the network were selected using the Xavier approach. The cascade feed-forward neural network is optimized using the LM training algorithm. The optimized cascade feed-forward neural network is further used to predict the optimized RF MEMS switch dimensions for the desired application. The network produces an accuracy of 94.9%. An RF MEMS switch was designed from the dimensions predicted by the cascade feed-forward neural network. The designed switch offers – 55 dB Isolation and – 0.2 dB Insertion. Eventually, an antenna was designed by incorporating identical switches which offer frequency reconfigurability
Explaining the Explainers in Graph Neural Networks: a Comparative Study
No full textFollowing a fast initial breakthrough in graph-based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domains. To sort out this plethora of alternative approaches, several studies have benchmarked the performance of different explainers in terms of various explainability metrics. However, these earlier works make no attempts at providing insights into why different GNN architectures are more or less explainable or which explainer should be preferred in a given setting. In this survey we fill these gaps by devising a systematic experimental study, which tests 12 explainers on eight representative message-passing architectures trained on six carefully designed graph and node classification datasets. With our results we provide key insights on the choice and applicability of GNN explainers, we isolate key components that make them usable and successful and provide recommendations on how to avoid common interpretation pitfalls. We conclude by highlighting open questions and directions of possible future research
Simple and fast modelling of radio frequency passives in view of beyond-5G and 6G applications: case study of an RF-MEMS multi-state network described by an equivalent lumped element network
No full textThe utilization of RF-MEMS, which stands for Microsystem-based (MEMS) Radio Frequency (RF) passive components, is garnering growing attention within the realm of Beyond-5G (B5G) and 6G technologies, despite its longstanding existence. This trend is fueled by the impressive RF characteristics achievable through the judicious exploitation of this technology. However, the complex interplay of various physical phenomena in RF-MEMS, spanning mechanical, electrical, and electromagnetic domains, renders the design and optimization of new configurations challenging. In this study, a modeling approach based on Lumped Element Networks (LEN) is employed to accurately predict the Scattering Parameters (S-parameters) characteristics of multi-state and highly reconfigurable RF-MEMS devices. The device under scrutiny is a multi-state RF step power attenuator, previously fabricated, tested, and documented in literature by the principal author. Although these physical devices exhibit flat attenuation characteristics, they are subject to certain non-idealities inherent to the technology. The refined LEN-based methodology presented herein aims to interpret and incorporate such undesirable parasitic effects to provide precise predictions for real RF-MEMS devices. Two custom metrics, referred to as Percent Magnitude Difference (PMD) and Percent Phase Difference (PPD), are utilized to evaluate the accuracy of the LEN model, revealing differences consistently within 1 and 8%, respectively, across a frequency range spanning from 100 MHz to 13.5 GHz
Feasibility Assessment of Aquifer Thermal Energy Storage: A Case Study in Riva del Garda, Italy
No full textDifferential cross-section measurements of D± and D±s meson production in proton-proton collisions at √s = 13 TeV with the ATLAS detector
No full textProgrammable Functional Connectivity and Synchronous Activity in Resistive Switching Self‐Assembled Nanostructured Networks
Get PDFThe efficiency of biological data processing systems is based on their adaptive connectivity and on the mutual interactions of their elements at different scales. These features are not substantially present in the design of electronic architectures based on conventional integrated circuits. The exploitation of self-assembled systems characterized by nonlinear dynamics is actively investigated as a viable alternative strategy to develop energy-efficient data processing devices. However, the encoding of external stimuli and the decoding of information from the analog response of such systems is still a challenge. Here we characterize the functional connectivity and the synchronicity between active sites in cluster-assembled nanostructured Au films showing resistive switching behavior by a combined approach based on micro-thermography and electrical measurements. We investigate the complex mechanisms involved in the network reorganization leading to its resistive switching activity and we identify the interplay between network dimensions and its emerging electrical behavior. We investigate the control on the synchronous activity and on the connectivity of the micrometric active sites which rule the emerging network dynamics and determe the performance of data processing devices. This activity is described using data analysis techniques commonly used in neuroscience, which are proposed for the first time to characterize neuromorphic systems
Structural Insights into the Mechanical Behavior of Large-Area 2D Covalent Organic Framework Nanofilms
No full textTwo-dimensional covalent organic frameworks (2D COFs) are periodic, permanently porous, lightweight solids with remarkable structural modularity, enabling precise control over their properties. As thin films, they have shown promising applications in chemical separations and organic electronics, making it crucial to understand their stability under mechanical stress. Here, we investigate how two different chemical linkages commonly used for 2D COFs, specifically imine and enamine, influence the mechanical properties of nanoscale thick films. Centimeter-scale 2D COF films with a thickness below 100 nm were synthesized by a condensation reaction at a liquid-liquid interface and subsequently transferred onto patterned substrates for mechanical testing. By employing a custom-made nanotensile testing platform, we achieved a comprehensive mechanical characterization of freestanding 2D COF films over a large area (0.5 mm2), a size relevant for device applications. The enamine-linked COF exhibits a higher Young's modulus and tensile strength but a lower fracture strain compared to the imine-linked COF, a difference attributed to the tightly stacked structure of the enamine-linked COF, as confirmed by molecular dynamics simulations. This distinct mechanical behavior reveals a fundamental relationship between the linkage chemistry of 2D COF and their mechanical properties, providing valuable insights that can drive the development of strong and durable thin-film devices based on 2D COFs
«A beneficio della nostra Camera e Decima». Boschi e foreste nel territorio trentino-tirolese (secoli XV-XVIII)»
Get PDFFino al 1803 il Trentino fu suddiviso tra il principato vescovile di Trento (66%) e la contea del Tirolo (30%), i cui territori facevano parte della giurisdizione politico-amministrativa dei "Confini italiani". Accanto alle forme di godimento dei beni collettivi da parte delle comunità, in alcune aree dalla fine del Quattrocento fu imposta la regalia forestale (Forstregal), che compromise i diritti sui boschi comunitari e feudali. Il contributo esamina le tappe di questo processo attuato attraverso la creazione di un apparato forestale incaricato di regolamentare lo sfruttamento e la commercializzazione dell’esteso patrimonio forestale spesso in aperta conflittualità con le comunità rurali