Miracle-dz/UniFAR-FTUs huggingface dataset:
Dense RGB-D SLAM with Planes Detection and Mapping

Observing the absence of predominant planar features in most previous RGB-D simultaneous localization and mapping (SLAM) systems, we introduce a dense RGB-D SLAM, which meanwhile detects and visualizes large planes in the reconstructed indoor scenes. The major challenges are threefold. Firstly, large indoor planes are usually partially observed. Moreover, loop-closure problems should not undermine those detected and reconstructed 3D planes. At last, the efficiency, especially the processing time analysis, is always a major concern in SLAM systems. To unravel these problems, we detect plane segments in each new observed depth map. The detected new and old planes are matched and updated in a frame-to-model fashion. Hence, our plane detection results are directly related to the reconstructed 3D scenes, which eliminates the influence of loop closures. We enhance the efficiency of our system by considering those detected integral plane segments instead of individual points during camera motion tracking. Furthermore, our system accelerates all the processes by heavily applying parallel computations. Experimental results demonstrate that our system can densely reconstruct 3D scenes with detected planes, which also achieves near real-time property.
Gram Matrices Formulation of Body Shape Motion: An Application for Depression Severity Assessment

We propose an automatic method to measure depression severity from body movement dynamics in participants undergoing treatment for depression. Participants in a clinical trial for treatment of depression were interviewed on up to four occasions at 7-week intervals with the clinician-administered Hamilton Rating Scale for Depression. Body movement was tracked using OpenPose from full-body video recordings of the interviews. Gram matrices formulation was used for body shape and trajectory representations from each video interview. Kinematic features were extracted and encoded for video based representation using Gaussian Mixture Models (GMM) and Fisher vector encoding. A multi-class SVM was used to classify the encoded body movement dynamics into three levels of depression severity: severe, mild, and remission. Accuracy was high for severe depression (68.57%) followed by mild depression (56%), and then remission (37.93%). The obtained results suggest that automatic detection of depression severity from body movement is feasible.
DART: Dynamic Bandwidth Distribution Framework for Virtualized Software Defined Networks

In this paper we address a network architecture that uses a combination of network virtualization and software defined networking in order to reduce complexity of network management and at the same time support high quality of service. Within this network architecture, we propose a framework to be able to dynamically distribute the network bandwidth to various services such that the network resources are utilized efficiently. In many industrial domains, multiple services may use the same hardware platform for the sake of a better resource utilization. Therefore, bandwidth distribution among the services should be done in an efficient way during runtime. We also develop an admission control in this framework which dynamically coordinates the bandwidth distributions based on requested quality of services. We show the applicability of the proposed framework by implementing it on a common SDN controller. Moreover, we conduct a set of experiments to show the performance of the proposed framework.
Virtual Platform of FPGA based MPSoC for Power Electronics Applications: OS simulation

Currently, the number of designs of control algorithms in Power Electronics in FPGAs based on System on Chips (SoCs) is rising. This is mainly due to the flexibility that allows to have a programmable logic where complex algorithms can be hardware accelerated, and a processor able to execute high level control algorithms in baremetal or in OS like Linux, both in the same chip. In addition, the use of HLS tools for making hardware designs has decreased hardware implementation time. However, the complexity of these systems makes the design and verification tasks difficult. A very efficient technique to fulfil this challenging SoC designs is HW/SW co-simulation. A critical feature in verification is simulation speed, which is mainly limited by the processor simulator, especially when the device has several cores, as it happens with MultiProcessor SoCs (MPSoC). Therefore, a very fast processor simulator, such as QEMU, is crucial to achieve a reasonable simulation speed. This work describes Virtual Platform based on QEMU, SystemC and HLS to co-simulate hardware and software designs in a fast way. To exemplify, a smart-switcher design for synchronization of microgrids is taken as case study. It controls the connection of a microgrid to mains when both of them have similar voltage and phase. It includes a dual PLL, which is implemented in HW and provides all the information to the control algorithm executed in the processor (Linux or baremetal). The proposed Virtual Platform is analyzed under different working conditions and compared with other solutions analyzing the simulation speed.
A FAHP and MPTCP Based Seamless Handover Method in Heterogeneous SDN Wireless Networks

To solve the problem of vertical handover in heterogeneous wireless networks, this paper proposes a seamless handover method for heterogeneous wireless networks based on software defined network (SDN) architecture. The proposed method requires SDN control plane to periodically collect attributes of wireless networks so as to evaluate network performance. Considering varied types of services have different requirements and users have different preferences, fuzzy analytic hierarchy process (FAHP) and multi-path transmission control protocol (MPTCP) are applied to select the most appropriate target network and execute seamless handover, respectively. Compared to existing methods, the proposed method jointly considers service requirements, user preferences, and mobility management for network selection and guarantees service continuity during vertical handover. Simulation results show that the proposed method makes a comprehensive decision on network selection. In terms of handover times, the proposed method achieves up to 17.4% and 4.5% reduction compared to received signal strength based algorithm and fuzzy multiple criteria group decision making based algorithm, respectively. The proposed method also maintains at least one subflow connected during the handover process and achieves seamless handover.
Predictive Current Control for an IPMSM Drive with a Small Film DC-Link Capacitor

This paper investigates a small film DC-link capacitor inverter driving a permanent magnet synchronous motor. Two control methods, including a d-q axis current control and a positive torque region current control are proposed to improve the torque dynamics of the drive system. In addition, a damping compensation method is used to improve the input power factor and reduce the input current harmonics. A current-loop predictive controller is proposed to improve the dynamic responses of the current-loop of an IPMSM. A digital signal processor, TMS 320LF2808, is used as a control center to execute the control algorithms. Experimental results can validate the correctness and feasibility of the proposed methods.
Phase-shifted Pulse Width Modulation with alternate zeros voltage for parallel connection of H-Bridges for High-Current Low-Voltage applications

Ahstract-Phase-Shifted Modulation (PSM) applied to multilevel converters is an effective method for reducing the output THD. In this paper, a new unipolar PSM method is proposed to control internal circulating current of a high-current low-voltage power converter, composed of parallel connected H- Bridges. The proposed method reduces the H-bridge currents distortions and the power losses of the overall converter. Simulation results comparing the standard unipolar modulation with the proposed unipolar modulation technique are performed to demonstrate its effectiveness.
Charge analysis for Li-ion battery pack state of health estimation for electric and hybrid vehicles

The aging monitoring of Lithium-ion batteries (LIBs) represents a critical point for electrified vehicle applications. Consumers may expect their vehicle to have a steady autonomy range and available power throughout the lifetime of their cars. Several embedded solutions exist in the literature. In this paper two new approaches are suggested to enrich the existing solutions. To that extent, capacity fading is studied using exchanged energy during charging events. What's more, power fading is assessed using direct current resistance (DCR) and voltage measurement at the beginning of charge events. Both solutions produce reliable state of health measurements SoH with significantly good accuracy.
Recurrent neural-linear posterior sampling for non-stationary bandits

An agent in a non-stationary multi-armed bandit problem should balance between exploration and the exploitation of (periodical or structural) patterns present in its previous experiences. Current successful agents require a handcrafted context that allows treating a non-stationary problem as a contextual multi-armed bandit problem. Unfortunately, even a carefully designed context may introduce spurious relationships or lack a convenient representation of crucial information. In order to address these issues, we propose an approach that learns to represent the relevant context for a decision based solely on the raw history of interactions between the agent and the environment. This approach relies on a combination of Bayesian recurrent neural networks and posterior sampling. Preliminary experiments show that our recurrent approach outperforms its feedforward counterpart in simple non-stationary problems when the handcrafted context is less than ideal.
University of Southern Denmark Assessment of biopsy-proven liver fibrosis by two-dimensional shear wave elastography

Herrmann, Eva; de Lédinghen, Victor; Cassinotto, Christophe; Chu, Winnie C-W; Leung, Vivian Y-F; Ferraioli, Giovanna; Filice, Carlo; Castera, Laurent; Vilgrain, Valérie; Ronot, Maxime; Dumortier, Jérôme; Guibal, Aymeric; Pol, Stanislas; Trebicka, Jonel; Jansen, Christian; Strassburg, Christian P; Zheng, Rongqin; Zheng, Jian; Francque, Sven; Vanwolleghem, Thomas; Vonghia, Luisa; Manesis, Emanuel K; Zoumpoulis, Pavlos; Sporea, Ioan; Thiele, Maja; Krag, Aleksander; Cohen-Bacrie, Claude; Criton, Aline; Gay, Joel; Deffieux, Thomas; Friedrich-Rust, Mireen
Tetramethylpyrazine Analogue T-006 Exerts Neuroprotective Effects against 6-Hydroxydopamine-Induced Parkinson's Disease In Vitro and In Vivo

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc), and there is no cure for it at present. We have previously reported that the tetramethylpyrazine (TMP) derivative T-006 exhibited beneficial effects in Alzheimer's disease (AD) models. However, its effect on PD remains unclear. In the present study, we investigated the neuroprotective effects and underlying mechanisms of T-006 against 6-hydroxydopamine- (6-OHDA-) induced lesions in in vivo and in vitro PD models. Our results demonstrated that T-006 alleviated mitochondrial membrane potential loss and restored the energy metabolism and mitochondrial biogenesis that were induced by 6-OHDA in PC12 cells. In addition, animal experiments showed that administration of T-006 significantly attenuated the 6-OHDA-induced loss of tyrosine hydroxylase- (TH-) positive neurons in the SNpc, as well as dopaminergic nerve fibers in the striatum, and also increased the concentration of dopamine and its metabolites (DOPAC, HVA) in the striatum. Functional deficits were restored following T-006 treatment in 6-OHDA-lesioned mice, as demonstrated by improved motor coordination and rotational behavior. In addition, we found that the neuroprotective effects of T-006 were mediated, at least in part, by the activation of both the PKA/Akt/GSK-3β and CREB/PGC-1α/NRF-1/TFAM pathways. In summary, our findings demonstrate that T-006 could be developed as a novel neuroprotective agent for PD, and the two pathways might be promising therapeutic targets for PD.
Neuroimmune-Mediated Alcohol Effects on Ventral Tegmental Area Neurons

Neuroimmune-Mediated Alcohol Effects on Ventral Tegmental Area Neurons Stephanie Bair Williams Neuroscience Center, BYU Master of Science Dopamine (DA) transmission is a key player in the rewarding aspects of ethanol as well as ethanol dependence. The current dogma is that DA transmission is increased during ethanol via the inhibition of ventral tegmental area (VTA) GABA neurons and that excitation of VTA GABA neurons during withdrawal results in decreased DA transmission. Microglia, the major neuroimmune effector in the brain, may be a key mediator in this process by releasing cytokines following activation. We evaluated the effect of ethanol on cytokine concentrations in the VTA and NAc using a cytometric bead array, and found that low dose ethanol (1.0 g/kg) decreased interleukin (IL)-10 levels, but high dose ethanol increased IL-10 levels (4.0 g/kg). We also used standard cell-attached mode electrophysiological techniques to evaluate the effects of select cytokines on VTA neuron firing rate in vitro. We found no change in firing rate in response to IL-6, but an increase in firing rate in VTA DA neurons response to IL-10. Consistent with the changes in firing rate, opticallyevoked IPSCs were also found to be decreased in response to IL-10. Ex vivo voltammetry and in vivo microdialysis were done to determine whether IL-10 can directly result in an increase in DA release. Although ex vivo voltammetry showed no change in DA release, IL-10 increased DA release in vivo. These findings suggest that the rewarding and/or addictive effects of ethanol are mediated by cytokines, specifically the anti-inflammatory cytokine IL-10.
FOR INCREASING HYDROLYSIS OF CELLULOSIC MATERIAL IN THE PRESENCE OF CELLOBIOSE DEHYDROGENASE Reference to a Sequence Listing

(51) International Patent Classification: AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, C12N 9/02 (2006.0 1) C12N 9/42 (2006.0 1) CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, C12N 9/24 (2006.01) D21C 5/00 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, (21) International Application Number: KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, PCT/US2009/068563 ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, (22) International Filing Date: NO, NZ, OM, PE, PG, PH, PL, PT, RO, RS, RU, SC, SD,
A Bias-Variance Decomposition for Bayesian Deep Learning

We exhibit a decomposition of the Kullback-Leibler divergence into terms corresponding to bias, variance, and irreducible error. Our particular focus in this work is Bayesian deep learning and in this domain we illustrate the application of this decomposition to adversarial example identification, to image segmentation, and to malware detection. We empirically demonstrate qualitative similarities between the variance decomposition and mutual information.
A Thesis for the Degree of Master of Science Molecular and Physiological Characterization of the Effect of Ralstonia solanacearum NLS-containing Type III Effectors on Immunity and Development in Nicotiana benthamiana and Arabidopsis thaliana Ralstonia solanacearum의 NLS 이펙터가

Ralstonia solanacearum is a soil-borne phytopathogen that causes lethal bacterial wilt in a wide range of food crops. Pathogenicity of R. solanacearum is mediated by the type III secretion system (T3SS) that injects type III effectors (T3Es) directly into host cells. T3Es are known to modulate not only plant immunity, but also various host cell processes for the bacterial infection. However, because of the complexity of R. solanacearum T3E repertoire, only few of them have been characterized for their mode of action. Here, I used heterologous expression of individual nuclear localization sequence (NLS)-containing R. solanacearum T3Es in Nicotiana benthamiana and Arabidopsis thaliana to screen the effectors that have effect on plant
An observational mixed-methods approach to investigate the fear of cancer recurrence cognitive and emotional model by Lee-Jones et al with women with breast cancer during radiotherapy treatment

There is minimal qualitative research on fear of cancer recurrence (FCR) in patients who are still undergoing treatment. This study explored how breast cancer patients’ illness beliefs changed during radiotherapy treatment, so as to provide their longitudinal perspective across sessions. These beliefs were mapped to Lee-Jones et al FCR model to assess its applicability to patients during this key treatment phase. A framework qualitative analysis was employed for verbatim interactions between patients (n = 8) and their radiographer (n = 2) over a minimum of three weekly review sessions (26 review consultations in total). Results proved suggested evolution and repetition of themes within and across sessions. Most themes were consistent with the early stages of the Lee-Jones et al model (antecedents and FCR) such as internal and external cues, cognitions and emotions. The crucial observation was that somatic stimuli were interpreted as side effects of radiotherapy treatment rather than cancer symptoms. Patients were still undergoing their last phase of major treatment, whereas the Lee-Jones et al model has been constructed to explain patients’ past treatment experience. New themes emerged, including current exercise, concurrent illnesses/problems, cancer treatment as a constant reminder (of diagnosis) and associated sleeping difficulties. Decatastrophising of symptoms and experiences relating to cancer and its treatment was also a prominent theme indicating a possible coping mechanism to reduce worries about treatment side effects and associated experiences. Finally, some evidence was found from failure of emotional/fear processing in patients due to early surface reassurance by health professionals – a possible explanation of how FCR might arise. Early detection of FCR and promoting support while patients are still undergoing treatment might prevent patients from developing FCR after treatment.
Robust Resource Allocation and Transmission Time Optimization for OFDMA-based Heterogeneous Networks

To improve transmission efficiency and robustness, we investigate robust energy efficiency (EE)-based maximization power allocation and time optimization problem in orthogonal frequency division multiple access (OFDMA) based heterogeneous networks under channel uncertainties. The non-convex optimization problem is decomposed into three subproblems: subcarrier allocation, transmission time optimization and power allocation. The subcarrier allocation is obtained based on maximizing user's EE under the effective transmission time. The Taylor expansion method is used to obtain the optimal transmission time. Additionally, Bernstein approach and Dinkelbach's method are used to solve the power allocation problem. Simulation results indicate that the proposed algorithm has good robustness in terms of outage probability.
Link to Published Version: PHOTOGRAMMETRIC TRACKING OF AERODYNAMIC SURFACES AND AEROSPACE MODELS AT NASA LANGLEY RESEARCH CENTER

Aerospace engineers require measurements of the shape of aerodynamic surfaces and the six degree of freedom (6DoF) position and orientation of aerospace models to analyse structural dynamics and aerodynamic forces. The measurement technique must be noncontact, accurate, reliable, have a high sample rate and preferably be non-intrusive. Close range photogrammetry based on multiple, synchronised, commercial-off-the-shelf digital cameras can supply surface shape and 6DoF data at 5-15Hz with customisable accuracies. This paper describes data acquisition systems designed and implemented at NASA Langley Research Center to capture surface shapes and 6DoF data. System calibration and data processing techniques are discussed. Examples of experiments and data outputs are described.
Toward Real Time Backward-Mode Pulsed Magnetomotive Ultrasound

Magnetomotive ultrasound (MMUS) is an emerging technique to image in vivo magnetic nanoparticle (MNP) distribution, which is challenging to conventional B-mode ultrasound, and has shown its potential in extravascular ultrasound molecular imaging, monitoring of magnetic nano-drug delivery, and sentinel lymph node identification. To date, MMUS is mainly implemented in forward mode where the imaging object must lie in between an ultrasound probe and an electromagnet, and the imaging frame rate is low, both of which hinder its clinical translation. To facilitate clinical translation of MMUS, here we move toward the development of a real time backward-mode pulsed magnetomotive ultrasound system based on an ultrasound research engine, Prodigy (S-Sharp Corp., Taiwan), featuring an MMUS probe integrating a linear array and an electromagnet. The MMUS probe facilitates the backward-mode MMUS, improving the portability of MMUS. All the processing exploits the fast Prodigy-to-MATLAB interface, PSRT, automatically streaming beamformed ultrafast baseband data to MATLAB for post-processing and image display. Overall, we demonstrate that the imaging frame rate of our MMUS system can achieve 3.4 Hz with free hand scanning, which is about nine time faster than those of the previously reported systems. We believe that such handheld and toward real-time capability would push MMUS toward clinical usage.
What Affects the Performance of Convolutional Neural Networks for Audio Event Classification

Convolutional neural networks (CNN) have played an important role in Audio Event Classification (AEC). Both 1D-CNN and 2D-CNN methods have been applied to improve the classification accuracy of AEC, and there are many factors affecting the performance of models based on CNN. In this paper, we study different factors affecting the performance of CNN for AEC, including sampling rate, signal segmentation methods, window size, mel bins and filter size. The segmentation method of the event signal is an important one among them. It may lead to overfitting problem because audio events usually happen only for a short duration. We propose a signal segmentation method called Fill-length Processing to address the problem. Based on our study of these factors, we design convolutional neural networks for audio event classification (called FPNet). On the environmental sounds dataset ESC-50, the classification accuracies of FPNet-1D and FPNet-2D achieve 73.90% and 85.10% respectively, which improve significantly comparing to the previous methods.
Real-Time Speed Control of a PMSM for Wind Turbine Application

This paper presents the real-time speed control of permanent magnet synchronous machine (PMSM) in a motoring mode for variable speed wind energy conversion system application. For this purpose, a discrete real-time laboratory test-rig is prepared using Permanent magnet synchronous servo motor, GUASCH Inverter and Texas instrument TMS320F28069 Digital Signal Processor (DSP) discrete real time microcontroller control card in a GUASCH board for implementing the designed PI current controller and PI speed controller. In addition, a simulation model of the system is prepared in MATLAB Simulink user-defined function blocks considering the dynamic mathematical equations of every part in the system. Finally, the achieved results of test-rig show that the system works very well for different transient speed changes. Moreover, it shows that the real-time time results validate the MATLAB simulation model results of the system.
Model Predictive Control of a regenerative Flying Capacitor Converter with reduced switch count

This paper proposes a new topology with reduced switch count for a regenerative flying capacitor multilevel inverter. This paper also introduces a new control strategy, the use of Predictive Control. The resulting control scheme is extremely simple to understand and to implement. In addition, simulation results present a very good performance of the converter.