Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Validation of Numerical Magnetic Force and Torque Model for Magnetically Levitated Actuator

To decouple the multi-axis motion in the 6 degrees of freedom magnetically levitated actuators (MLAs), this paper introduces a numerical method to model the force and torque distribution. Taking advantage of the Gaussian quadrature, the concept of coil node is developed to simplify the Lorentz integral into the summation of the interaction between each magnetic node in the remanence region and each coil node in the coil region. Utilizing the coordinate transformation in the numerical method, the computation burden is independent of the position and the rotation angle of the moving part. Finally, the experimental results prove that the force and torque predicted by the numerical model are rigidly consistent with the measurement, and the force and torque in all directions are decoupled properly based on the numerical solution. Compared with the harmonic model, the numerical wrench model is more suitable for the MLAs undertaking both the translational and rotational displacements.
Probabilistic Music-Symbol Spotting in Handwritten Scores

Content-based search on musical manuscripts is usually performed assuming that there are accurate transcripts of the sources in a symbolic, structured format. Given that current systems for Handwritten Music Recognition are far from offering guarantees about their accuracy, this traditional approach does not represent a scalable scenario. In this work we propose a probabilistic framework for Music-Symbol Spotting (MSS), that allows for content-based music search directly over the images of the manuscripts. By means of statistical recognition systems, a probabilistic index is built upon which the search can be carried out efficiently. Our experiments over a dataset of an Early handwritten music manuscript in Mensural notation demonstrates that this MSS framework can be presented as a promising alternative to the traditional approach for content-based music search.
Text Line Segmentation for Challenging Handwritten Document Images using Fully Convolutional Network

This paper presents a method for text line segmentation of challenging historical manuscript images. These manuscript images contain narrow interline spaces with touching components, interpenetrating vowel signs and inconsistent font types and sizes. In addition, they contain curved, multi-skewed and multi-directed side note lines within a complex page layout. Therefore, bounding polygon labeling would be very difficult and time consuming. Instead we rely on line masks that connect the components on the same text line. Then these line masks are predicted using a Fully Convolutional Network (FCN). In the literature, FCN has been successfully used for text line segmentation of regular handwritten document images. The present paper shows that FCN is useful with challenging manuscript images as well. Using a new evaluation metric that is sensitive to over segmentation as well as under segmentation, testing results on a publicly available challenging handwritten dataset are comparable with the results of a previous work on the same dataset.
Optimizing the Placement of Data Collection Services on Vehicle Clusters

Vehicles are an important source of data, with embedded sensors including built-in cameras. Their data can be processed close to where it was generated, using computation and network resources on collaborating vehicles. Processing at the network edge helps to reduce end-to-end service latencies. However, such mobile virtual resource pools require effective management, especially if those resources are to be leased to third-party service providers. We address the problem of service placement on moving, connected, vehicle nodes (V2V) supported by roadside infrastructure (V2I). We formulate a distributed service model and optimize the placement of services, subject to constraints related to node resource capacity, link capacity, distributed application deployment (full deployment, anti-collocation and adjacency constraint) and vehicle mobility.
Echocardiographic Particle Image Velocimetry in Heart Diseases

Echocardiographic particle image velocimetry (Echo-PIV) is an emerging technique to provide us useful information of the left ventricular (LV) blood flow and vortex in various heart diseases. Vortex flow in vivo is extremely complicated and affected by many factors and also has several roles. Factors affect on LV vortex formation are LV size and geometry, myocardial mechanics, electro-mechanical sequence, hemodynamics, and the mitral valve. Essential roles of vortex are 1) a temporary kinetic energy reservoir, 2) blood flow redirection, 3) closure of the mitral valve, and 4) diastolic filling facilitator. Understanding the LV blood flow and vortex may be useful in many clinical situations for heart diseases such as heart failure, myocardial infarction, and valvular diseases. There is also a report that bi-ventricular pacing optimizes the LV diastolic filling by optimizing the timing of the onset of the diastolic vortex from the mitral valve opening. Understanding of LV blood flow and vortex has a clinical potential for early diagnosis, risk stratification, and treatment of heart diseases.
A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing

Air-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies greatly on the illumination condition due to color based segmentation. In a less fluctuating illumination condition the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework achieved 97.7%, 95.4% and 93.7% recognition rate in person independent evaluation over English, Bengali and Devanagari numerals, respectively.
Topological Interference Management for Wireless Networks

Topological interference management (TIM) is considered under the condition that only topology-related information is available at the transmitters, but no accurate channel state information is fed back to them, which is capable of significantly reducing the overhead imposed on the network. TIM schemes can be classified into two broad categories, relying either on a fixed topology or on alternating connectivity. In this paper, we review the family of TIM schemes. Specifically, for the fixed topology, we discuss the attainable degrees of freedom (DoFs) under the condition that the channel coefficient values are either constant or time varying. For constant channel coefficients, we discuss the multiple groupcast networks/multiple unicast networks, interference channel networks, and interference broadcast channel networks; while for time-varying channel coefficients, we discuss interference channel networks and interference broadcast channel networks. Furthermore, in the context of alternating connectivity, we discuss the attainable DoF for interference channel networks, as well as X networks, vector broadcast channel networks, and interference broadcast channel networks. Finally, promising research directions are identified for TIM schemes.
Constrained and Parametric Dynamic Programming for Word Image Retrieval

There have been many published works on using Dynamic Time Warping (DTW) for word image matching. It has also been shown that constrained DTW works better than classical DTW, due to it's property to limit the warping path and to avoid pathological matching. Moreover, some studies also demonstrate that variants of DTW can perform better, compared to classical DTW. To take advantages from these techniques, we propose in this paper to study how combination of such approaches can even more improve the results. First, we propose to use pseudo Local DTW (LDTW: more efficient than DTW in our experiments) with a constraint band. Next, several other sequence matching techniques are parametrically combined to study their potential advantages. For this, we adapted the approach to the context of word image matching to obtain the value of the parameters. It is shown that, at least on Bentham and George Washington datasets, results can be improved by combining some algorithms. Please note that the goal of this research work is not to propose a competitive word spotting technique but to propose a dynamic programming based learning free approach for word spotting or image matching, which can perform moderately with respect to other existing approaches in this domain.
A Novel Data Integrity Attack Detection Algorithm Based on Improved Grey Relational Analysis

False data injection (FDI) attack is the most common data integrity attack, and it is also one of the most serious threats in industrial control systems (ICSs). Although many detection approaches are developed with burgeoning research interests, the technical capability of existing detection methods is still insufficient because the stealth FDI attacks have been proven to bypass bad data detector. In this paper, a novel data analytical algorithm is proposed to identify the stealth FDI attacks in ICSs according to the correlation analysis. First, we evaluate the correlation between measurements and control variables based on an improved grey relational analysis. Then, SVM is used to classify the FDI attack according to the values of correlation. Through a reliable semi-physical simulation testbed whose virtual plant corresponds to a 330 MW boiler-turbine unit, two FDI attacks that can bypass the detection system are studied. A dataset, which contains the normal data and attack data, is created from the testbed to verify the effectiveness of the proposed algorithm. In addition, the performance of the proposed algorithm is also studied based on the new gas pipeline dataset that is collected by the distributed analytics and security institute in Mississippi State University. Such a novel algorithm, which has better accuracy and reliability, is compared with the state of the art based on the data analysis.
Adaptive Thresholding Algorithm for Noisy Electrocardiograms using Reverse Biorthogonal Mother Wavelets

Electrocardiography aids physicians to investigate the electrical activity of the heart. Fetal signals are difficult to record and usually affected by noise, making thus a clinical diagnosis difficult. A robust and easy to implement denoising algorithm has been proposed in the present paper. The method is based on adaptive wavelet thresholding, the best mother wavelet of the reverse biorthogonal family being searched. The results are competitive and the performance has been evaluated both graphically and with objective criteria.
A Critique of Little Box Challenge Inverter Designs: Breaking from Traditional Design Tradeoffs

Since the introduction of wide-bandgap (WBG) power semiconductor devices, high power density (PD) inverter design has become an increasingly popular topic in modern research literature. The 2014-2015 Little Box Challenge (LBC) cosponsored by Google and the IEEE sparked even more interest in this area because WBG-based designs aspired to achieve upward of 215-W/in<sup>3</sup> power densities. This article reviews several key topics of importance that are related to realizing such high PD gallium nitride (GaN) WBG-based inverter designs. More specifically, this article includes a comprehensive survey of technologies employed by participating LBC finalists in their efforts to achieve new levels of PD in 400-Vdc to single-phase 240-Vac conversion applications while maintaining tight specifications on dc ripple, ac ripple, and aggressive requirements on efficiency. Both singlephase dc-to-ac conversion and the related active energy buffer circuit topologies used by all LBC finalists are documented and compared. Additionally, a graphical trend line and figure of merit (FOM) are developed that compare PD vis-? -vis efficiency of all proposed solutions. These are used to gain insight into how particular designs were able to achieve relatively higher PD levels and to illustrate how the GaN-based design from the University of Illinois at Urbana-Champaign (UIUC) was able to achieve the highest density power conversion in this competition. Furthermore, enhanced auxiliary devices and techniques used in the CE+T and Virginia Tech (VT) designs are discussed as well.
Deep Neural Network Structured Sparse Coding for Online Processing

Sparse coding, which aims at finding appropriate sparse representations of data with an overcomplete dictionary set, has become a mature class of methods with good efficiency in various areas, but it faces limitations in immediate processing such as real-time video denoising. Unsupervised deep neural network structured sparse coding (DNN-SC) algorithms can enhance the efficiency of iterative sparse coding algorithms to achieve the goal. In this paper, we first propose a sparse coding algorithm by adding the idea “weighted” in the iterative shrinkage thresholding algorithm (ISTA), named WISTA, which can enjoy the benefit of the <inline-formula> <tex-math notation="LaTeX">$l_{p}$ </tex-math></inline-formula> norm <inline-formula> <tex-math notation="LaTeX">$(0 < p < 1)$ </tex-math></inline-formula> sparsity constraint. Then, we propose two novel DNN-SC algorithms by combining deep learning with WISTA and the iterative half thresholding algorithm (IHTA), which is the <inline-formula> <tex-math notation="LaTeX">$l_{0.5}$ </tex-math></inline-formula> norm sparse coding algorithm. Furthermore, we present that by changing the loss function, the DNN can be learned supervisedly and unsupervisedly. Unsupervised learning is the key to ensure the DNN to be learned online during processing, which enables the use of the DNN-SC algorithms in applications lacking labels for signals. Synthetic data experiments show that WISTA can outperform ISTA and IHTA. Moreover, the DNN-structured WISTA can successfully achieve converged results of WISTA. In real-world data experiments, the procedure of utilizing DNN-SC algorithms in image denoising is first presented. All DNN-SC algorithms can accelerate at least 45 times while maintaining PSNR results compared with their corresponding sparse coding algorithms. Finally, the strategy of utilizing DNN-SC algorithms in real-time video denoising is presented. The video-denoising experiments show that the DNN-structured ISTA and WISTA can conduct real-time video denoising for 25 frames/s <inline-formula> <tex-math notation="LaTeX">$360\times 480$ </tex-math></inline-formula> pixels gray-scaled videos.
A Cascaded Spatial Transformer Network for Oriented Equipment Detection in Thermal Images

Equipment detection is a fundamental step towards automatic diagnosis in electric power industry. In this paper, we propose a novel approach to detect equipment parts, which are often oriented in thermal images due to the use of hand-held thermographic cameras. The proposed approach integrates a cascaded spatial transformer network (STN) and a proposal-driven detection network together. The cascaded STN introduces a rotation transform globally so that the rigid body constraint between parts is implicitly considered. Moreover, the cascaded scheme is also robust to large variation in orientation. The designed STN and the detection network are first trained separately to get good initialization weights. Further, these two networks are fine-tuned in an end-to-end manner. Experiments demonstrate that both the cascaded scheme and the end-to-end fine-tuning highly boost the detection performance.
Towards End-to-End 3D Human Avatar Shape Reconstruction from 4D Data

We propose a novel approach for human avatar shape reconstruction from point cloud sequences. Our approach requires no pretrained human shape model and generates a subject specific deformable shape model. Contrary to existing template-based methods, we optimize all model parameters jointly on the GPU with an off-the-shelf optimizer to deal with a large amount of free variables. Preliminary qualitative evaluation shows promising results.
Reliability in GaN-based devices for power applications

This paper analyzes two important reliability issues in AlGaN/GaN devices: positive bias temperature instability (PBTI) and time-dependent dielectric breakdown (TDDB). The summarized results of our previous PBTI studies in MOS-HEMTs show that the threshold voltage degradation in devices with SiO2 as gate dielectric is characterized by a universal decreasing behavior of the trapping rate parameter and is ascribed to charge trapping in the SiO2 and at the SiO2/GaN interface. On the contrary, the degradation observed in Al2O3- and AlN/Al2O3-gate stacks is mainly attributed to charge capture in the pre-existing dielectric traps with a negligible interface state generation. Additionally, the insertion of a thin AlN layer impacts on the device reliability because larger trap density, faster charge trapping, wider trap energy distribution and slower charge release are observed compared with devices without this layer. The dielectric importance of GaN-based devices has been also investigated in Schottky Barrier Diodes (SBDs) with a gated edge termination (GET). Our recent TDDB results indicate a narrower Weibull distribution, and a longer time to failure in devices with a double GET layer structure and with a thick passivation layer (2 GET-THICK) than in single GET devices with a thin passivation (1 GET-THIN). Therefore, the former structure is more suitable for high-power and high-temperature applications.
Robust  ${L_{2}} - {L_{\infty}}$  Filter Design for Uncertain 2-D Continuous Nonlinear Delayed Systems With Saturation

This paper discusses the <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_\infty }$ </tex-math></inline-formula> filter design problem for non-linear two-dimensional (2-D) uncertain continuous systems with state delays and saturation. The non-linear function under consideration is assumed to satisfy the Lipschitz condition while the saturation term is being dealt by using a memory-less sector region methodology. A suitable Lyapunov-Krasovskii functional is considered, and the Wirtinger-based integral inequality method is used to derive some sufficient conditions which ensure that the resultant filtering error system is robustly asymptotically stable along-with the specified <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_\infty }$ </tex-math></inline-formula> disturbance attenuation level <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>. A suitable example explains the derived results’ usefulness.
An Investigative Analysis of Different LSTM Libraries for Supervised and Unsupervised Architectures of OCR Training

Optical Character Recognition (OCR) involves conversion of images of text into machine encoded editable text. Despite the wide research advancements in the field of OCR systems, the recognition capability of OCR systems on unseen or degraded historical documents is still questionable. The degradations in the document like torn pages, ink spread and blurred documents are major challenges especially in the old paper documents. Most of such degraded documents lack a generalized and reliable OCR system mainly because of the unavailability of ground-truth data and poor generalization capabilities of the OCR systems. Also manually transcribing the documents is cumbersome task which also require certain language-specific expertise. This paper presents a feasibility study of different OCR architectures together with different preprocessing stages for a reliable OCR on such challenging documents. To this end, we evaluate various OCR settings on a dataset containing highly degraded historical German typewriter documents. This paper investigates various key aspects of OCR training such as the impact of incorporation of different LSTM libraries, grayscale or binarized data for training and training data size used on the subject dataset. In addition, difference in the effect of using completely manually transcribed data as compared to semi-corrected ground-truth data for anyOCR architecture of unsupervised OCR training have been analyzed on a small dataset. The anyOCR framework has shown promising results as an efficient OCR system which was evident with its comparison with other OCR systems. The various factors analyzed provided a feasible strategy for approaching the problem and evaluating highly challenging historical documents.
A Pulse Frequency Modulation VCO-ADC in 40 nm

This brief describes the architecture and implementation of a novel voltage-controlled oscillator-based analog-to-digital converter (VCO-ADC). Instead of a ring oscillator, the VCO is built with a pulse frequency modulation architecture where an analog feedback loop ensures both oscillation and linearity of the voltage-to-frequency conversion. A multibit first-order noise-shaped output is achieved by sampling a digital delay line that acts as a part of the oscillator and as a multibit quantizer. A prototype has been implemented in a 40-nm CMOS process. Although most of the circuit elements are taken from a standard digital library, a transconductor is required as the input stage. To ensure proper linearity, a bulk-driven transconductor has been designed. The ADC measurements reach 53 dB of SNDR at 1 GHz sampling frequency in a 20-MHz bandwidth with a pseudo-differential architecture. Powered at 1.1 V, the power consumption is 3.5 mW. The active area is 0.08 mm2. The resulting figure-of-merit equals 242 fJ/step.
A Dataset for Performance Analysis of the Social Internet of Things

Node, service and information discovery as well as trust management are key issues that characterize the IoT when huge numbers of nodes have to collaborate to support the deployed applications. A recent promising proposal, with the ability to address these issues, is the Social IoT (SIoT) paradigm, whose main principle is to enable objects to autonomously establish social links with to each other (adhering to rules set by their owners). To be able to test and validate this ability, significant datasets regarding objects' networks (node description, typology, activities, exchanged traffic) are needed, which however are not completely available. This paper addresses this issue by presenting a dataset that has been realized on the basis of real IoT objects available in the city of Santander and categorized following the typologies and data model for objects introduced in the FIWARE Data Models. Object profiles and guidelines for the relationships' creation for the SIoT are described and the obtained data and the resulting social network is made available to the research community.
A Fast Thermo-Piezoelectric Finite Element Model of 3D Transient FBAR Dynamics Under Large RF Signal

As high power RF filtering applications become increasingly relevant, there is a need to quickly predict the effect of nonlinear interactions between thermal and piezoelectric fields. Yet, traditional finite element methods (FEM) for 3D piezoelectric resonators impose unreasonable memory and time requirements. Here, we develop a fast finite element model of thermo-piezoelectric physics based on a generalized series expansion of the governing equations. Together with transient, thermal IR measurements of film bulk acoustic resonator (FBAR) under large signal, the model is used to explain a range of nonlinear phenomena reported here for the first time.