ENIDH – EEM - Artigo Científico
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- A metaplectic perspective of the uncertainty principle in the Linear Canonical Transform domainPublication . Dias, C. Nuno; Gosson, Maurice de; Prata, João Nuno; ElsevierWe derive Heisenberg uncertainty principles for pairs of Linear Canonical Transforms of a given function, by resorting to the fact that these transforms are just metaplectic operators associated with free symplectic matrices. The results obtained synthesize and generalize previous results found in the literature, because they apply to all signals, in arbitrary dimension and any metaplectic operator (which includes Linear Canonical Transforms as particular cases). Moreover, we also obtain a generalization of the Robertson-Schrödinger uncertainty principle for Linear Canonical Transforms. We also propose a new quadratic phase-space distribution, which represents a signal along two intermediate directions in the time-frequency plane. The marginal distributions are always non-negative and permit a simple interpretation in terms of the Radon transform. We also give a geometric interpretation of this quadratic phase-space representation as a Wigner distribution obtained upon Weyl quantization on a non-standard symplectic vector space. Finally, we derive the multidimensional version of the Hardy uncertainty principle for metaplectic operators and the Paley-Wiener theorem for Linear Canonical Transforms.
- Enhanced AutomaticWildfire Detection System Using Big Data and EfficientNetsPublication . Fernandes, Armando; Utkin, Andrei; Chaves, PauloPrevious works have shown the effectiveness of EfficientNet—a convolutional neural network built upon the concept of compound scaling—in automatically detecting smoke plumes at a distance of several kilometres in visible camera images. Building on these results, we have created enhanced EfficientNet models capable of precisely identifying the smoke location due to the introduction of a mosaic-like output and achieving extremely reduced false positive percentages due to using partial AUROC and applying class imbalance. Our EfficientNets beat InceptionV3 and MobileNetV2 in the same dataset and achieved a true detection percentage of 89.2% and a false positive percentage of only 0.306% across a test set with 17,023 images. The complete dataset used in this study contains 26,204 smoke and 51,075 non-smoke images. This makes it one of the largest, if not the most extensive, datasets reported in the scientific literature for smoke plume imagery. So, the achieved percentages are not only among the best reported for this application but are also among the most reliable due to the extent and representativeness of the dataset.
- Adiabatic radial perturbations of relativistic stars: Analytic solutions to an old problemPublication . Luz, Paulo; Carloni, SanteWe present a new system of equations that fully characterizes adiabatic, radial perturbations of perfect fluid stars within the theory of general relativity. The properties of the system are discussed, and, provided that the equilibrium spacetime verifies some general regularity conditions, analytical solutions for the perturbation variables are found. As illustrative examples, the results are applied to study perturbations of selected classical exact spacetimes, and the first oscillation eigenfrequencies are computed. Exploiting the new formalism, we derive an upper bound for the maximum compactness of stable, perfect fluid stars, which is equation-of-state agnostic and significantly smaller than the Buchdahl bound.
- Gauge invariant perturbations of static spatially compact LRS II spacetimesPublication . Luz, Paulo; Carloni, SanteWe present a framework to describe completely general first-order perturbations of static, spatially compact, and locally rotationally symmetric class II spacetimes within the theory of general relativity. The perturbation variables are by construction covariant and identification gauge invariant and encompass the geometry and the thermodynamics of the fluid sources. The new equations are then applied to the study of isotropic, adiabatic perturbations. We discuss how the choice of frame in which perturbations are described can significantly simplify the mathematical analysis of the problem and show that it is possible to change frames directly from the linear level equations. We find explicitly that the case of isotropic, adiabatic perturbations can be reduced to a singular Sturm–Liouville eigenvalue problem, and lower bounds for the values of the eigenfrequencies can be derived. These results lay the theoretical groundwork to analytically describe linear, isotropic, and adiabatic perturbations of static, spherically symmetric spacetimes
- A Tunable Gain and Bandwidth Low-Noise Amplifier with 1.44 NEF for EMG and EOG Biopotential SignalPublication . Vieira, Rafael; Näf, Fabian; Martins, Ricardo; Horta, Nuno; Lourenço, N.; Póvoa, RicardoThis paper presents a low-noise inverter-based current-mode instrumentation amplifier with tunable gain and bandwidth for electromyogram (EMG) and electrooculogram (EOG) biopotential signals, targeting low input noise while maintaining low power consumption. The gain tuning method is based on pseudo-resistors, whereas the bandwidth is tunable due to a varactor system that is controlled by the same control voltage that tunes the gain. The circuit was designed and manufactured using the 110 nm UMC CMOS technology node, occupying an area of 0.624 mm2. The circuit presents a functioning mode for each biopotential signal with different characteristics, for the EMG a gain of 34.7 dB and a bandwidth of 1412 Hz was measured, with an input referred noise of 1.407 μV which matches a noise efficiency factor of 1.44. The EOG mode achieves a 39.5 dB gain and a 22.4 Hz bandwidth while presenting an input-referred noise of 0.829 μV corresponding to a noise efficiency factor of 6.37. For both modes, the supply voltage is 1.2 V and the circuit consumes 1 μA.
- Manufacturing Calcium Phosphates Scaffolds Using 3D Printed Lost MoldsPublication . Albardeiro, Miguel; Sousa, Adriana; Guedes, Mafalda; Marat-Mendes, Rosa; Pina, Célia; Figueiredo, Lígia; Ascenso, Eduardo; Baptista, Ricardo
- Architectural Design for Heartbeat Detection Circuits using Verilog-A Behavioral ModelingPublication . Vieira, R.; Passos, F.; Póvoa, R.; Martins, R.; Horta, N.; Guilherme, J.; Lourenço, N.This work presents a study of two analog frontend circuit architectures for heartbeat detection. Both circuits present an amplification block as the first stage, followed by a band-pass filter. In the first, the heartbeat detection is done using an adaptive threshold based on pulse-width, whereas the heartbeat detection in the second is done using a sample and hold to find the maximum and minimum peak of each beating. Both architectures are modeled in Verilog-A and simulated using real-world ECG signals with different characteristics. This work studies possible fundamental analog circuit blocks suitable for wearable implementation. It evaluates critical performances requirements from the analysis of the behavior simulations. It was verified that the first circuit can properly detect heartbeats as long as the input-referred noise is below 21 μV, whereas the second one ensures it until 30 μV. The low cutoff frequency can be approximately 10 Hz without compromising the signal’s peaks, which means that these specifications can be relaxed substantially compared to systems intended to reconstruct the signal accurately.
- Shortening the Gap between Pre- and Post-Layout Analog IC Performance by Reducing the LDE-induced Variations with Multi-Objective Simulated Quantum AnnealingPublication . Martins, Ricardo; Lourenço, Nuno; Póvoa, Ricardo; Horta, NunoThe design of analog and mixed-signal integrated circuits (ICs) is intricate due to the continuous nature of the signals handled. Still, it is also strongly affected by the physical implementation of analog devices on the circuits’ layout. The circuit layout corresponds to the physical implementation of an analog IC used in fabrication that describes its devices geometrically. As circuits’ integration and device sizes shrink, the physics of the interactions between devices, as they are placed in the layout, was proved to easily drive analog and mixed-signal ICs from promising pre-layout performances to completely post-layout malfunction. As these layout-dependent effects (LDEs) can only be evaluated once the layout is completed, the true post-layout performance is only evaluated in a late stage of the traditional design flow, causing expensive redesign iterations lacking the information that identifies precisely where, in the layout, there are problems needed to be solved. For technologies above the 40-nanometers, the leading causes of LDEs are mobility and threshold voltage variations. This paper proposes an automatic device placement methodology that explicitly accounts for, and minimizes, these LDEs. An absolute representation of the floorplan is adopted, and, multiple optimization techniques, including the novel, constrained archive-based multi-objective implementation of the simulated quantum annealing inspired algorithm, enhanced with specific LDE-impact mitigation operators are applied to solve the problem. In each of these optimization processes, established LDE formulations for accurate circuit simulation models are used to evaluate each candidate placement solution, and, guide the optimization process. In the case of multi-objective implementations, ultimately offering a realistic perspective of the LDE-aware design tradeoffs between performance deterioration and used chip area. Experimental results conducted over state-of-the-art analog structures on a challenging 65-nanometers technology node show that the proposed methodology shortens the gap between pre- and post-layout performance by reducing the LDE-induced variations, aiming for first-time-right layout design
- DeepPlacer: A Custom Integrated OpAmp Placement Tool using Deep ModelsPublication . Gusmão, António; Póvoa, Ricardo; Horta, Nuno; Lourenço, Nuno; Martins, RicardoMechanisms towards the automatic analog integrated circuit layout design have been an intensive research topic in the past few decades. Still, the industrial environment has no automatic approach established. The advances of machine learning applications in electronic design automation come with the promise to change this reality. This paper proposes a deep learning generative model for the placement ‘‘optimization’’ of analog integrated circuit basic blocks. The model behaves as an argmin operator for the placement cost function and can provide placement solutions instantly. Moreover, the model can be fed with unlabeled data, greatly facilitating data collection. A generic and innovative circuits’ representation at the network’s input layer is proposed, encoding the devices’ dimensions, connectivity, and topological constraints. Besides, the randomness found in generative models is embedded directly into the feature vector, as the order of the features per device is shuffled in the input vector. Shuffling the order of the devices’ features in the input not only brings multi-modality but also solves a generalization problem, as there is not any natural order defined to place devices in the feature vector. As a proof of concept, a deep artificial neural network capable of proposing different placement solutions, in less than 150 ms each, for six amplifier topologies and, in multiple technology nodes ranging from 350 nm down to 65 nm, is demonstrated. DeepPlacer was capable of producing correct solutions for topologies and technology nodes not present in the training set, showing good generalization while not hindering circuit performance due to the placement
- FUZYE: A Fuzzy C-Means Analog IC Yield Optimization using Evolutionary-based AlgorithmsPublication . Canelas, António; Póvoa, Ricardo; Martins, Ricardo; Lourenço, Nuno; Guilherme, Jorge; Carvalho, João Paulo; Horta, NunoThis paper presents fuzzy c-means-based yield estimation (FUZYE), a methodology that reduces the time impact caused by Monte Carlo (MC) simulations in the context of analog integrated circuits (ICs) yield estimation, enabling it for yield optimization with population-based algorithms, e.g., the genetic algorithm (GA). MC analysis is the most general and reliable technique for yield estimation, yet the considerable amount of time it requires has discouraged its adoption in population-based optimization tools. The proposed methodology reduces the total number of MC simulations that are required, since, at each GA generation, the population is clustered using a fuzzy c-means (FCMs) technique, and, only the representative individual (RI) from each cluster is subject to MC simulations. This paper shows that the yield for the rest of the population can be estimated based on the membership degree of FCM and RIs yield values alone. This new method was applied on two real circuit-sizing optimization problems and the obtained results were compared to the exhaustive approach, where all individuals of the population are subject to MC analysis. The FCM approach presents a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. Moreover, a k-means-based clustering algorithm was also tested and compared with the proposed FUZYE, with the latest showing an improvement up to 13% in yield estimation accuracy