Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Fault-Prognostic Approach Based on Interacting Multiple Model Filters and Fuzzy Systems

An interacting multiple model (IMM) filter is a recognized method for adaptive estimation of states that is often necessary to characterize the behavior of dynamic systems with a multiple-mode operation. The traditional IMM filter adopts the measurement set to update the information about the active models. However, when this approach is used for fault-prognostic applications, it can lead to misleading state estimation. To overcome this problem, this paper proposes a novel fault-prognostic technique based on a modified IMM filter with fuzzy systems. The main methodological contribution of this paper is to build an approach based on an IMM filter able to make long-term fault predictions without measurements. This is done by incorporating a fuzzy system into the proposed IMM filter with the objective of modeling the system's dynamics and updating the probabilities of the observed modes. Furthermore, the proposed method and the standard IMM filter are compared in a numerical example and a real experimental platform PRONOSTIA for validation. The results analysis indicates a better prediction performance than the conventional IMM filter considering the failure time predictions and a measurement model not properly adjusted.
Pansharpening for Multiband Images With Adaptive Spectral–Intensity Modulation

The pansharpening algorithm often faces an imbalance between spatial sharpness and spectral preservation, resulting in spectral and intensity inhomogeneities in the fused image. In this paper, to overcome this problem, we present a robust pansharpening method for multiband images with adaptive spectral–intensity modulation. In this method, we propose an adaptive spectral modulation coefficient (ASMC) and an adaptive intensity modulation coefficient (AIMC) to modulate the spectral and spatial information in the fused image, respectively. Among these coefficients, the ASMC is constructed based on two aspects: first, the details extracted from the panchromatic (PAN) and multispectral (MS) images; and second, the spectral relationship between each MS band. The AIMC is calculated by assessing the correlation and standard deviation between the PAN image and each MS band. Finally, we propose a mathematically linear model to combine ASMC and AIMC to achieve the fused image. Various remote-sensing satellite images were used in the evaluations. Experimental results indicate that the proposed method achieves outstanding performance in balancing spatial and spectral information and outperforms several state-of-the-art fusion methods in terms of both full-reference and no-reference metrics, and on visual inspection.
Dynamic Organization's Representation. Linking Project Management with Enterprise Architecture

The business sustainability of the organizations requires a representation of their business and IT architecture to sustain business decisions as well as to plan transformation initiatives necessary for competitiveness in the market. However, with current approaches and tools, keeping updated and reliable architectures of business and IT has been proven to be a major effort consuming activity. The paper presents an approach to use project and portfolio management processes as information sources to update automatically organization's business and IT architecture representations. Such architectural views has a major impact on the capacity that organizations have to plan new projects as well as to better execute ongoing projects.
A Dual-Winding Fault-Tolerant Motor Drive System Based on the Redundancy Bridge Arm

In order to adapt to the single power supply system of an aircraft, this paper proposes and investigates a novel dual-winding fault-tolerant (DF) motor drive system based on the redundancy bridge arm for aerospace applications. The proposed DF motor offers the advantages of magnetic isolation, physics isolation, thermal isolation, and small cogging torque ripple, inhibiting the short-circuit current and high fault tolerance. The proposed DF motor drive can adapt to the single power supply system of the aircraft, reduce the drive cost, and reduce the number of independent power sources and power switches. In addition, it can improve the power density, the reliability, and the utilization rate of the drive. At the same time, the drive system has stronger fault-tolerant ability, and it can achieve the fault-tolerant control for a one-phase-winding fault or a multiphase-winding fault. To realize the short-circuit fault diagnosis performance, the diagnosis and processing method of the short-circuit fault has been investigated. Then, to realize the short-circuit fault-tolerant performance, a novel space vector pulsewidth modulation (SVPWM) fault-tolerant control strategy based on the short-circuit fault diagnosis and processing method is proposed. Finally, the performances of the DF motor drive have been verified by experiments. The research results verify the reliability and effectiveness of the proposed DF motor drive system based on the redundancy bridge arm and the SVPWM fault-tolerant control strategy.
Marginal Stacked Autoencoder With Adaptively-Spatial Regularization for Hyperspectral Image Classification

Stacked autoencoder (SAE) provides excellent performance for image processing under sufficient training samples. However, the collection of training samples is difficult in hyperspectral images. Insufficient training samples easily make SAE overfit and limit the application of SAE to hypersepctral images. To address this problem, a novel marginal SAE with adaptively-spatial regularization (ARMSAE) is proposed for hyperspectral image classification. First, a superpixel segmentation method is used to divide the image into many homogenous regions. Then, at the pretraining stage, an adaptively-shaped spatial regularization is introduced to extract contextual information of samples in the homogenous regions. It sufficiently utilizes unlabeled adjacent samples to alleviate the lack of training samples. At the fine-tuning stage, the marginal samples based on geometrical property are selected to tune the ARMSAE network. The fine-tuning exploits margin strategy to alleviate the inaccurate statistical estimation caused by insufficient training samples. Finally, the label of each test sample is determined by all the samples locating in the same homogenous region. Experimental results on hyperspectral images demonstrate the proposed method provides encouraging classification performance compared with several related state-of-the-art methods.
New Directions for Racial Discrimination Research as Inspired by the Integrative Model

In 1996, Cynthia García Coll and colleagues introduced a groundbreaking model that articulated factors that influenced the development of ethnic−racial minority and immigrant youth. One of the key arguments was that racial discrimination is a stratification mechanism resulting in negative developmental outcomes for minority youth, and this argument has been supported by several meta-analyses. Against the backdrop of the rise of White nationalism, increasing hate crimes against ethnic−racial minority individuals, implementation of national policies that disproportionately affect communities of color, and the shift to a majority–minority country, it is imperative for future research to refine key aspects of the integrative model that are intricately connected to these trends. The present article offers recommendations for future research in 4 areas: (1) expansion of segregation indicators to include mass incarceration and mass deportation, (2) expansion of the study of resilience factors among minority youth, (3) application of the integrative model to White youth, and (4) the development of comprehensive measures and utilization of diverse methodologies. It is imperative that future research clarify these issues to further understanding of the development of all children in the context of racial discrimination.
Optimized Extreme Learning Machine for Big Data Applications Using Python

This paper reports an optimized implementation for the extreme learning machine (ELM). Among several modeling languages, Python with optimized linear algebra library support was found to be the best choice. A novel nonlinear function, the absolute value, is proposed and shown to provide the best performance and speed among other traditional nonlinearities. A fixed point implementation with finite resolution is proposed and evaluated, concluding that best accuracy is maintained for 2 bits quantization in the input layer and 8 bits in the output layer. The optimized ELM with hardware-convenient nonlinearities and finite precision is widely suitable for solving big data problems on various computing platforms such as FPGA, low cost microcontrollers, etc. In terms of speed, our optimized ELM solution outperforms state of the art Python implementations, providing at least 3 times faster training and retrieval on similar computing platforms.
Robust Vehicle Infrastructure Cooperative Localization in Presence of Clutter

One of the primary challenges for a successful Highly Assisted and/or Autonomous Vehicle is its localization. To improve the precision of location of the vehicle, not only the internal sensors are being used, but also using data from external sensors is attracting increasing attention from the research community. One such proposed sensor is an infrastructure RADAR which can be used to improve the localization of the ego-vehicle. Although a RADAR indeed is a supplementary source of information, it suffers a unique type of clutter which have trajectories like real objects and can therefore result in “ghost measurements”, i.e., measurements which do not correspond to any real vehicles. This deteriorates the quality of the fused state estimates. This paper proposes a robust method to fuse the RADAR readings in presence of such outliers. This methodology builds upon a previously proposed solution where the problem was formulated as a factor graph. The RADAR measurements were added as a novel constraint of sum of inter-vehicle distance, called Topology Factor. Our previous work assumed clutter free environment. This paper proposes a novel robust Topology Factor which is also resilient against above mentioned outliers. Simulations (based on real data) show promising results in the direction of lowering the degradation of fused state estimates in presence of such clutter.
A Novel Practice for Face Classification

This paper presents a correspondence measure method based on novel method which combines a permutation of Voila Jones Face detection method and Eigen face classification (Identification) technique. In the proposed method, we come to overcome the problem of low accuracy. We propose a new system here to align the face using Voila Jones Face detection method followed by Eigen face classification technique. Compared to other face detection methods, the proposed method is very efficient for the face detection purpose. The system is built to meet real time face classification criteria.
Developmental and Repairing Production of Myelin: The Role of Hedgehog Signaling

Since the discovery of its role as a morphogen directing ventral patterning of the spinal cord, the secreted protein Sonic Hedgehog (Shh) has been implicated in a wide array of events contributing to the development, maintenance and repair of the central nervous system (CNS). One of these events is the generation of oligodendrocytes, the glial cells of the CNS responsible for axon myelination. In embryo, the first oligodendroglial cells arise from the ventral ventricular zone in the developing brain and spinal cord where Shh induces the basic helix-loop-helix transcription factors Olig1 and Olig2 both necessary and sufficient for oligodendrocyte production. Later on, Shh signaling participates in the production of oligodendroglial cells in the dorsal ventricular-subventricular zone in the postnatal forebrain. Finally, the modulation of Hedgehog signaling activity promotes the repair of demyelinated lesions. This mini-review article focuses on the Shh-dependent molecular mechanisms involved in the spatial and temporal control of oligodendrocyte lineage appearance. The apparent intricacy of the roles of two essential components of Shh signaling, Smoothened and Gli1, in the postnatal production of myelin and its regeneration following a demyelinating event is also highlighted. A deeper understanding of the implication of each of the components that regulate oligodendrogenesis and myelination should beneficially influence the therapeutic strategies in the field of myelin diseases.
Measuring Two-Factor Authentication Schemes for Real-Time Data Access in Industrial Wireless Sensor Networks

Dozens of two-factor authentication schemes have been proposed to secure real-time data access in industrial wireless sensor networks (WSNs). However, more often than not, the protocol designers advocate the merits of their scheme, but do not reveal (or unconsciously ignoring) the facets on which their scheme performs poorly. Such lack of an objective, comprehensive measurement leads to the unsatisfactory “break-fix-break-fix” cycle in this research area. In this paper, we make an attempt toward breaking this undesirable cycle by proposing a systematical evaluation framework for schemes to be assessed objectively, revisiting two foremost schemes proposed by Wu et al. (2017) and Srinivas et al. (2017) to reveal the challenges and difficulties in designing a sound scheme, and conducting a measurement of 44 representative schemes under our evaluation framework, thereby providing the missing evaluation for two-factor schemes in industrial WSNs. This work would help increase awareness of current measurement issues and improve the scientific process in our field.
Volume Scattering Losses Evaluation for Radar Sounding of Jovian Icy Moons

The icy crusts of the Jovian moons Europa, Ganymede and Callisto will be explored in the next future by orbiting spacecrafts hosting onboard Ice Penetrating Radars (IPRs). The volumetric scattering associated to a layer of icy regolith could potentially affect the propagation of the radar signal in the subsurface. We evaluate the efficiency of such phenomenon and the associated losses for the two relevant frequencies of 9 and 60 MHz. Our results demonstrate that for the lower frequency there will be no significative losses in the radar propagation, even in the most pessimistic scenario. Conversely, for the higher frequency, a very thick layer of regolith could reasonably give rise to a severe attenuation; therefore the performances at 60 MHz will be strictly governed by the local geology of the sounded area.
Cubature and Quadrature Based Continuous-Discrete Filters for Maneuvering Target Tracking

A continuous-discrete system represents a system with continuous process and discrete measurement models. Very recently, two filters namely cubature quadrature Kalman filter (CQKF) and Gauss-Hermite filter (GHF) are introduced to solve the filtering problems for discrete-time systems where both the process and measurement models are discrete in nature. In this paper we extend the two estimators so that they could work for continuous process and discrete measurement model. The proposed filters are applied to solve a continuous-discrete maneuvering air-traffic-control problem and the results are compared with continuous-discrete Kalman filter (CD-CKF) in terms of the root mean square error (RMSE). It has been found that the proposed methods provide better estimation accuracy compared to the CD-CKF.
Time-Based Sensing for Reference-Less and Robust Read in STT-MRAM Memories

This paper introduces the concept of time-based sensing (TBS) for bitcell read in spin transfer torque magnetic RAMs arrays. The TBS scheme converts the bitline voltage into time, then the sense amplifier discriminates the two bitcell levels in the time domain. The TBS scheme substantially improves the read yield compared to conventional voltage sensing (CVS). As further advantage, TBS requires no analog reference generation and distribution by leveraging the implicit timing reference set by the gate delay in the sense amplifier. Monte Carlo simulations in 65 nm show that the proposed TBS improves the read bit error rate (BER) by two-three orders of magnitude, compared to CVS. This is achieved at the cost of less than 1% area penalty and 13–14% performance degradation, and insignificant (2%) energy penalty when designed at iso-area (minimum delay). Compared to other sensing schemes at iso-BER, the proposed TBS scheme achieves a more favorable area-robustness-energy-performance tradeoff.
Multiple Roles of Glycans in Hematological Malignancies

The three types of blood cells (red blood cells for carrying oxygen, white blood cells for immune protection, and platelets for wound clotting) arise from hematopoietic stem/progenitor cells in the adult bone marrow, and function in physiological regulation and communication with local microenvironments to maintain systemic homeostasis. Hematological malignancies are relatively uncommon malignant disorders derived from the two major blood cell lineages: myeloid (leukemia) and lymphoid (lymphoma). Malignant clones lose their regulatory mechanisms, resulting in production of a large number of dysfunctional cells and destruction of normal hematopoiesis. Glycans are one of the four major types of essential biological macromolecules, along with nucleic acids, proteins, and lipids. Major glycan subgroups are N-glycans, O-glycans, glycosaminoglycans, and glycosphingolipids. Aberrant expression of glycan structures, resulting from dysregulation of glycan-related genes, is associated with cancer development and progression in terms of cell signaling and communication, tumor cell dissociation and invasion, cell-matrix interactions, tumor angiogenesis, immune modulation, and metastasis formation. Aberrant glycan expression occurs in most hematological malignancies, notably acute myeloid leukemia, myeloproliferative neoplasms, and multiple myeloma, etc. Here, we review recent research advances regarding aberrant glycans, their related genes, and their roles in hematological malignancies. Our improved understanding of the mechanisms that underlie aberrant patterns of glycosylation will lead to development of novel, more effective therapeutic approaches targeted to hematological malignancies.
High Dynamic Range Image Compression Based on Visual Saliency

High dynamic range (HDR) image has larger luminance range than conventional low dynamic range (LDR) image, which is more consistent with human visual system (HVS). Recently, JPEG committee releases a new HDR image compression standard JPEG XT. It decomposes input HDR image into base layer and extension layer. However, this method doesn’t make full use of HVS, causing waste of bits on imperceptible regions to human eyes. In this paper, a visual saliency based HDR image compression scheme is proposed. The saliency map of tone mapped HDR image is first extracted, then is used to guide extension layer encoding. The compression quality is adaptive to the saliency of the coding region of the image. Extensive experimental results show that our method outperforms JPEG XT profile A, B, C, and offers the JPEG compatibility at the same time. Moreover, our method can provide progressive coding of extension layer.
3-D TCAD Assessment of Fin-Based Hybrid Devices Under Heavy Ion Irradiation in 20-nm Technology

In this paper, the single event transient responses in nanometer hybrid FinFETs like wavy and symmetric dual- ${k}$ spacer (SDS) wavy FinFETs are systematically investigated. The novel SDS wavy FinFETs consist of an ultra-thin body, FinFET, and spacer engineering on a single silicon-on-insulator platform. Three-dimensional quantum simulations are used to study the heavy ion impact on 20 nm n-type hybrid devices. The physical insights on the post ion irradiation of the devices are carefully observed and compared with the conventional FinFETs. The effects of heavy ion irradiation on various parameters including current density, ion generation, transient current, and bipolar amplification are estimated. Furthermore, the post-ion irradiation is also examined as a function of ion characteristic time and ion-track radius.
A recurrent multi-scale approach to RBG-D Object Recognition.

Technological development aims to produce generations of increasingly efficient robots able to perform complex tasks. This requires considerable efforts, from the scientific community, to find new algorithms that solve computer vision problems, such as object recognition. The diffusion of RGB-D cameras directed the study towards the research of new architectures able to exploit the RGB and Depth information. The project that is developed in this thesis concerns the realization of a new end-to-end architecture for the recognition of RGB-D objects called RCFusion. Our method generates compact and highly discriminative multi-modal features by combining complementary RGB and depth information representing different levels of abstraction. We evaluate our method on standard object recognition datasets, RGB-D Object Dataset and JHUIT-50. The experiments performed show that our method outperforms the existing approaches and establishes new state-of-the-art results for both datasets.
Gas Friction Loss in Stator Ventilation Ducts of the Drive Motor for the Cooling Medium in a High-Temperature Gas-Cooled Reactor

The driving motor for the cooling medium in a high-temperature gas-cooled reactor is crucial for the coolant circulation in the reactor. This paper proposes a method for calculating the gas friction loss using Bernoulli's equation to study its effect on temperature rise in the motor. The ventilation network model of the motor was developed using the fluid network method, and a three-dimensional mathematical model of the stator was established by considering the relationships between the fluid velocity, temperature, and gas friction loss. The results of the ventilation system calculations were applied to the physical model as boundary conditions, and the distribution of the stator temperature was determined using the finite-volume method. The effect of the change in the stator ventilation structure on the gas friction loss distribution was studied. The calculated friction losses accorded well with the values obtained using the empirical formula for the gas friction loss.