Miracle-dz/UniFAR-FTUs huggingface dataset:
Defect Detection from 3D Ultrasonic Measurements Using Matrix-free Sparse Recovery Algorithms

In this paper, we propose an efficient matrix-free algorithm to reconstruct locations and size of flaws in a specimen from volumetric ultrasound data by means of a native 3D Sparse Signal Recovery scheme using Orthogonal Matching Pursuit (OMP). The efficiency of the proposed approach is achieved in two ways. First, we formulate the dictionary matrix as a block multilevel Toeplitz matrix to minimize redundancy and thus memory consumption. Second, we exploit this specific structure in the dictionary to speed up the correlation step in OMP, which is implemented matrix-free. We compare our method to state-of-the-art, namely 3D Synthetic Aperture Focusing Technique, and show that it delivers a visually comparable performance, while it gains the additional freedom to use further methods such as Compressed Sensing.
A Preconditioned Graph Diffusion LMS for Adaptive Graph Signal Processing

Graph filters, defined as polynomial functions of a graph-shift operator (GSO), play a key role in signal processing over graphs. In this work, we are interested in the adaptive and distributed estimation of graph filter coefficients from streaming graph signals. To this end, diffusion LMS strategies can be employed. However, most popular GSOs such as those based on the graph Laplacian matrix or the adjacency matrix are not energy preserving. This may result in a large eigenvalue spread and a slow convergence of the graph diffusion LMS. To address this issue and improve the transient performance, we introduce a graph diffusion LMS-Newton algorithm. We also propose a computationally efficient preconditioned diffusion strategy and we study its performance.
A Coupled-Inductor-Based Buck–Boost AC–DC Converter With Balanced DC Output Voltages

With the development of distributed power generation sources and the widely used dc characterized loads, the dc nanogrid becomes more and more attractive and the converters with three terminal outputs are increasingly studied. Considering the costs, the efficiency, and also the safety, the grounding configuration needs to be addressed when designing the ac–dc converter for a dc nanogrid system. An ac–dc converter with three terminal outputs has been presented for the united grounding configuration based dc nanogrid. Nevertheless, for this type of converters, the output voltages are unbalanced in case of unequal dc loads. This paper proposes a novel buck–boost ac–dc converter with the capacity of output voltages self-balancing by using a coupled inductor. The operation of this converter will be presented in details through analyzing its equivalent circuits. The small signal model of the system in different working modes is given, and the whole system control diagram shows how to balance the dc output voltages. A 220 V/50 Hz/1.5 kW prototype has been developed. Experiments are carried out to verify the effectiveness of the coupled-inductor-based converter.
Multi-Resolution Coded Apertures Based on Side Information for Single Pixel Spectral Reconstruction

Compressive spectral imaging (CSI) architectures allow to reconstruct spectral images from a lower number of measures than the traditional scanning-based methods. In these architectures, the coded aperture design is critical to obtain high-quality reconstructions. The structure of coded apertures is traditionally designed without information about the scene, but recently side information-based architectures provide prior information of the scene, which enables adaptive coded aperture designs. This work proposes the development of an adaptive coded aperture design for spectral imaging with the single pixel camera, based on a multi-resolution approach. An RGB side image is used to define blocks of similar pixels, such that they can be used to design the coded aperture patterns. This approach improves the reconstruction quality in up to $23\mathbf{dB}$ compared with traditional single pixel camera, and the computation time in up to 99.5% because it does not require an iterative algorithm.
Virtualized Network Services Extension Algorithms

This work presents algorithms to extend tenant Virtualized Network Functions Forwarding Graphs (VNF-FG) to embed new networking functions and introduce gradually additional services into the already deployed tenant dedicated slices. The related service graph extension problem is addressed through an Integer Linear Programming (ILP) algorithm that serves as a reference for performance comparisons with a proposed heuristic algorithm. An eigendecomposition approach is proposed, evaluated and compared.
The influence of carbon in the back-barrier layers on the surface electric field peaks in GaN Schottky diodes

Technology Computer Aided Design simulations are used to assess the influence of carbon in the back-barrier layers of GaN-on-Si wafers on the voltage distribution in GaN Schottky diodes. It is shown that carbon cannot be present as an acceptor only – as it is commonly assumed. The carbon needs to be compensated by donors or partly electrically inactive in order to explain the observed high hard breakdown voltage in GaN-onSi Schottky diodes. Furthermore, it is shown that the level of donor compensation of the carbon will have a significant influence on the two-dimensional voltage distribution in the devices, and, hence, on the surface electric field peaks. This conclusion is important to consider in the design of field plate extensions of the Schottky diode.
Effect of Random Sampling on Noisy Nonsparse Signals in Time-Frequency Analysis

The paper examines the exact error of randomly sampled reconstructed nonsparse signals having a sparsity constraint. When signal is randomly sampled, it looses the property of sparsity. It is considered that the signal is reconstructed as sparse in the joint time-frequency domain. Under this assumption, the signal can be reconstructed by a reduced set of measurements. It is shown that the error can be calculated from the unavailable samples and assumed sparsity. Unavailable samples degrade the sparsity constraint. The error is examined on nonstationary signals, with the short-time Fourier transform acting as a representative domain of signal sparsity. The presented theory is verified on numerical examples.
The Voltage Balancer with Two Phase Interleaved Bi-Directional Converter for ESS Application in Bi-Polar LVDC Distribution

This paper presents the voltage balancer with two phase interleaved bi-directional converter based on input parallel output series(IPOS). The proposed voltage balancer is restructured for ESS application in series to respond to voltage drop by using energy stored in energy storage system(ESS). Primary switching operations of proposed voltage balancer are based on bi-directional converter. As results of these methods, bi-polar voltage poles in LVDC distribution can be controlled by single ESS.
Joint Optimization of Molecular Resource Allocation and Relay Positioning in Diffusive Nanonetworks

We consider a realistic two-hop diffusion-based molecular communication (DbMC) system with spherical absorbing receivers in the presence of molecular degradation and noises. We address the problem of joint optimization of molecules allocation and relay location for the given detection thresholds in order to minimize the error probability of budget limited DbMC system. Numerical and simulation results reveal the improvement in error performance when molecules distribution and relay placement are in accordance with their joint optimal value. Moreover, it is found that as the relay detection threshold increases, more molecules are needed to be allocated to the source while relay need to be placed closer to the destination in order to satisfy the optimization criteria. Eventually, we demonstrate the effectiveness of our optimization solution through 3D and contour plots illustrating the convergence time.
Time-Dependent Pre-attention Model for Image Captioning

The task of automatically generating image captions draws a lot of attention in the past few years because it shows great potential in a wide range of application scenarios. The encoder-decoder structure with attention mechanism has been extensively applied to solve this task. However, most researches apply attention mechanism only to pay attention to image features but neglect the relations between image features which we think play an important role in scene understanding. To tackle this problem, we propose a novel attention mechanism named “attention to Time-Dependent Pre-Attention” (TDPA-attention) and the TDPA-attention is combined with a hierarchical LSTM decoder to compose our captioning model (TDPA-model). Within our TDPA-attention, at every time step, every image feature pays attention to all image features according to a semantic context and the attended feature is treated as an aggregated feature that contains relations between this image feature and all image features. All these aggregated features form a new feature set that the hierarchical LSTM decoder attends to. We evaluate our model on public image caption dataset Microsoft COCO and achieve state-of-the-art performance on most evaluation metrics.
A Novel Piecewise Nonlinear Companding Transform for PAPR Reduction in GFDM

Similar to the widely used orthogonal frequency division multiplexing (OFDM) scheme, Generalized Frequency Division Multiplexing (GFDM) system suffers from the problem of the high peak-to-average power ratio (PAPR). In this paper, we propose an efficient piecewise nonlinear companding transform for PAPR reduction of GFDM signals and a novel iterative receiver of GFDM to improve the bit error rate (BER) performance. By carefully adjusting transform parameters and the number of iterations of the receiver, more flexibility in the companding form and a better trade-off between PAPR reduction and BER performance can be achieved to satisfy various system requirements. Compared with existing PAPR reduction schemes in GFDM, simulation results show that the proposed technique can substantially offer the better performance of GFDM system in terms of PAPR reduction and BER performance.
Big Data for Operational Efficiency of Transport and Logistics: A Review

In the new information and communication era, digital transformation and adoption of recent technological advances have become a must for all transport and logistics providers who aim to significantly improve their activities. Consequently, this digitalization is inevitably giving birth to voluminous and rapidly growing sets of large-scale data generated from heterogeneous data sources, also known as Big Data. With particular management infrastructures and advanced data analysis methodologies, these huge amounts of data can be efficiently harvested to optimize the logistics and transport operations and provide a higher quality of service. This paper provides a review of the application of Big Data technologies in improving the operational efficiency of transport and logistics, exposes the main use cases and identifies some future research challenges.
Order Parameter and Band Gap of ZnSnN2

As the exploration for new solar absorbers continues, particularly those comprised largely of earth-abundant elements, heterovalent multinary compounds are often of interest. Such materials can be prepared in a variety of ways, but in high-quality crystalline layers, cation ordering can tend to replace unwanted secondary phases as a primary potential concern. Intriguingly, the ability to control the ordering of the cation sublattice introduces a new opportunity to adjust the band gap of such materials as we demonstrate in the case of ZnSnN2. Consequently, this new semiconductor shows promise as a narrow-gap solar absorber.
Thompson Sampling for Noncompliant Bandits

Thompson sampling, a Bayesian method for balancing exploration and exploitation in bandit problems, has theoretical guarantees and exhibits strong empirical performance in many domains. Traditional Thompson sampling, however, assumes perfect compliance, where an agent's chosen action is treated as the implemented action. This article introduces a stochastic noncompliance model that relaxes this assumption. We prove that any noncompliance in a 2-armed Bernoulli bandit increases existing regret bounds. With our noncompliance model, we derive Thompson sampling variants that explicitly handle both observed and latent noncompliance. With extensive empirical analysis, we demonstrate that our algorithms either match or outperform traditional Thompson sampling in both compliant and noncompliant environments.
Radial source velocity estimation using multiple line spectrum signals based on compressive sensing

A target's radial velocity estimation technique with single receiver in shallow water is proposed. Based on normal mode theory, the periodic oscillation characteristics of cross-correlated pressure field at two separated ranges are analyzed. The interference period of the cross-correlated field contains information of radial source velocity, which can be obtained by Fourier transform for broadband continuous spectrum signals. Aiming at multiple line spectrum signals, compressive sensing is applied to extract the interference period of the cross-correlated field and estimate the radial source velocity. The Basis Pursuit De-Noising(BPDN) algorithm is used in simulation and sea trial data processing. The results show that the proposed algorithm can achieve radial source velocity effectively, it does not require any knowledge of the waveguide environment and is suitable for uniform and variable speed moving target.
Efficacy of isothiocyanate-based compounds on different forms of persistent pain

Purpose Current pharmacotherapy for persistent pain related to neuropathy or articular diseases is unsatisfactory, due to the large number of unresponsive patients and side effects. Isothiocyanates (ITCs) are a class of natural or synthetic compounds characterized by the general formula R–NCS. ITCs show antihyperalgesic effects in models of central and peripheral nervous tissue injury and anti-inflammatory properties. The pharmacodynamics are strictly related to the release of the gasotransmitter hydrogen sulfide (H2S) from their moiety. In particular, phenyl ITC (PITC) and 3-carboxyphenyl ITC (3C-PITC) exhibit interesting slow H2S-release properties suitable for treating painful pathology. The aim of the present work was to evaluate the efficacy of PITC and 3C-PITC against mechanical hyperalgesia and spontaneous pain induced by nerve injury and osteoarthritis. Methods Nerve injury and osteoarthritis were induced in rats by ligation of the sciatic nerve (chronic constriction injury) and intra-articular injection of monoiodoacetate, respectively. Behavioral tests were performed 14 days after damage induction. Results Single subcutaneous administrations of PITC, 3C-PITC (4.43 and 13.31 µmol kg−1, respectively) were able to completely reverse hypersensitivity to noxious stimuli in both models of neuropathic and osteoarticular pain. The effect of ITCs was compared with that of NaHS, the prototypical H2S donor, showing similar efficacy and higher potency. ITCs and NaHS also reduced spontaneous pain. Conclusion ITCs offer a promising novel approach to counteract persistent, drug-resistant painful pathology.
Language Identification Using Gender Dependent GMM-UBM for Three Indian Languages

Automatic Language identification is important for a multilingual country like India. The work described in this paper deals with Language identification system (LID) for three Indian languages-Hindi, Telugu and Urdu, using Mel Frequency Cepstral Coefficient (MFCC) features. In this work, Universal Background Model (UBM) is built using MFCC features taken from all the languages which make it language and speaker independent. Two cases are considered here. First one is a gender independent Gaussian mixture model-Universal background model (GMM-UBM) and the second one is gender dependent model. Higher accuracy of 83.3% is obtained for gender dependent LID system compared to gender independent model.
Predictors of nodal metastasis and prognostic significance of lymph node ratio and total lymph node count in tracheobronchial adenoid cystic carcinoma

Purpose To identify potential risk factors of lymph node metastasis and to verify the prognostic significance of the lymph node ratio (LNR) and the total number of lymph nodes examined (NNE) in tracheobronchial adenoid cystic carcinoma (ACC) patients, using a large population-based database. Patients and methods From Surveillance, Epidemiology, and End Results database, we identified 263 patients with tracheobronchial ACC in whom complete lymph node data could be obtained. Logistic regression analysis was performed to determine predictive factors of nodal metastasis. X-tile software determined the optimal cut-off points for LNR and NNE. Kaplan– Meier analyses and Cox regression models were adopted for survival analysis. Results Of 263 patients, 75 (28.5%) had lymph node involvement. Tumors of bronchial origin (P<0.001) and tumors larger than 30 mm (P<0.001) were associated with a higher likelihood of nodal involvement. Examination of more than ten lymph nodes could avoid understaging and resulted in improved survival; meanwhile, patients with a LNR of 0.07 or less had favorable prognosis. Conclusion Patients with tracheobronchial ACC have significant risk of lymph node metastasis. Bronchial ACC and larger tumor size are both risk factors of lymph node metastasis. LNR and NNE may provide a more precise prediction of survival and could be taken into account in future clinical work.
Modeling Nitric Oxide Induced Neural Activity and Neurovascular Coupling in a Cerebellum Circuit

Functional magnetic resonance imaging (fMRI) allows to identify brain regions activated during rest, normal and diseased conditions attributing a function or a task to microcircuit activity. In order to understand signals from fMRI techniques, bottom-up modeling would be needed to reconstruct the neurovascular coupling attributing neural activity to local blood flow changes. In this paper, we present a bottom-up mathematical modeling of neuro-vascular coupling in rat cerebellum granule cells. Granule cells are numerous and main source for NO production in cerebellum. While matching experimental estimations, the effect of cerebellar blood flow and neural activity, a model of nitric oxide (NO) in the cerebellar granular was simulated and the diffusion, production and consumption of NO at the synapse and the contribution for neuro vascular coupling was modeled. This paper showcases the first step attributing cerebellum sub-molecular changes to clinical observations reconstructing neural activity mappings to population responses when combined with BOLD modeling.
A Benchmark for Full Rotation Head Tracking

This paper introduces a new benchmark for 360-degree rotation head tracking, named Full Rotation Head Tracking (FRHT). The benchmark consists of 50 color sequences containing diverse human activities with complicated head motions. Specially, FRHT covers the most challenges of head tracking and focuses on the appearance variations of heads during the 360-degree rotation. It also pays attention to the clutters from the heads of nearby people. Further, we propose a baseline tracker. It guides a selective adaption updating by verifying strategies, thus alleviates error accumulation. Extensive experiments validate the advantages of FRHT in head rotation and similar object clutter.
2D-to-3D Facial Expression Transfer

Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops.
Simulation of Tubular Transverse Flux Permanent Magnet Linear Generator

Tubular transverse flux permanent magnet linear generator is especially suitable for wave energy conversion because of its special movement forms and high power density. Generator design and optimization rely on finite element simulation tools. However, the existing commercial finite element tools are not friendly supported to the tube-type linear generator simulation. This paper tries to find a way to simplify the tubular TF linear generator in order to reduce the simulation time under the premise of ensuring the accuracy of simulation results.
A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification

Various representation-based methods have been developed and shown great potential for pattern classification. To further improve their discriminability, we propose a Bi-level optimization framework in terms of both low-dimensional projection and collaborative representation. Specifically, during the projection phase, we try to minimize the intra-class similarity and inter-class dissimilarity, while in the representation phase, our goal is to achieve the lowest correlation of the representation results. Solving this joint optimization mutually reinforces both aspects of feature projection and representation. Experiments on face recognition, object categorization and scene classification dataset demonstrate remarkable performance improvements led by the proposed framework.
An Analysis of the Distribution of Inter-Flash Time Intervals in the Area of the Säntis Tower

In this paper we analyzed the time intervals between flashes in the Säntis area for three highly active storms and also for a period of 21 months. The overall distributions of inter-flash time interval are found to be very similar and reasonably represented by an exponential probability distribution function. The mean value of the inter-flash interval for the 21-month long period is 23 seconds. About half of the flashes occurred within 5 seconds of the previous flash.
Generalized Gaussian Approximation and Its Application in Polar Decoding

How to analyse the block error rate (BLER) of state-of-the-art successive cancellation list (SC-list) decoders has been the most appealing and challenging problem in polar coding theory. In this paper, by investigating the distributions of path metrics for each path at each decoding stage, a generalized Gaussian approximation method is proposed to obtain the distributions of log-likelihood ratio (LLR) messages calculated in a SC-list decoder. Moreover, we also adopt Monte Carlo method to validate the proposed method, based on which a framework to analyze the BLER performance of SC-list decoding is developed. Finally, numerical results show that the proposed generalized Gaussian approximation method approaches the real distribution of LLR messages in practical SC-list decoding.