Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Varying Vibration Compensation Based on Segmented Interference for Triangular FMCW LiDAR Signals

Frequency modulation continuous wave (FMCW) light detection and ranging (LiDAR) 3D imaging system may suffer from time-varying vibrations which will affect the accuracy of ranging and imaging of a target. The system uses only a single-period FMCW LiDAR signal to measure the range of each spot; however, traditional methods may not work well to compensate for the time-varying vibrations in a single period because they generally assume the vibration velocity is constant. To solve this problem, we propose a time-varying vibration compensation method based on segmented interference. We first derive the impact of time-varying vibrations on the range measurement of the FMCW LiDAR system, in which we divide the time-varying vibration errors into primary errors caused by the vibrations with a constant velocity and quadratic errors. Second, we estimate the coefficients of quadratic vibration errors by using a segmented interference method and build a quadratic compensation filter to eliminate the quadratic vibration errors from the original signals. Finally, we use the symmetrical relations of signals in a triangular FMCW period to estimate the vibration velocity and establish a primary compensation filter to eliminate the primary vibration errors. Numerical tests verify the applicability of this method in eliminating time-varying vibration errors with only a one-period triangular FMCW signal and its superiority over traditional methods.
Extensive Study of Cloud Computing Technologies, Threats and Solutions Prospective

Infrastructure as a Service (IaaS) provides logical separation between data, network, applications and machines from the physical constrains of real machines. IaaS is one of the basis of cloud virtualization. Recently, security issues are also gradually emerging with virtualization of cloud computing. Different security aspects of cloud virtualization will be explored in this research paper, security recognizing potential threats or attacks that exploit these vulnerabilities, and what security measures are used to alleviate such threats. In addition, a discussion of general security requirements and the existing security schemes is also provided. As shown in this paper, different components of virtualization environment are targets to various attacks that in turn leads to security issues compromising the whole cloud infrastructure. In this paper an overview of various cloud security aspects is also provided. Different attack scenarios of virtualization environments and security solutions to cater these attacks have been discussed in the paper. We then proceed to discuss API security concerns, data security, hijacking of user account and other security concerns. The aforementioned discussions can be used in the future to propose assessment criteria, which could be useful in analyzing the efficiency of security solutions of virtualization environment in the face of various virtual environment attacks.
The taxonomic position of Anoplotaenia dasyuri (Cestoda) as inferred from molecular sequences

Abstract Anoplotaenia dasyuri Beddard, 1911 (Cestoda), from the Tasmanian devil, Sarcophilus harrisii (Boitard, 1842), is a taxonomic enigma, where a combination of morphological features, host type and geographical location have prevented it from being placed within a family and it is considered incertae sedis, despite its accepted validity. We performed a phylogenetic analysis of three A. dasyuri specimens collected from three Tasmanian devils using 18S and 28S rRNA sequences. Anoplotaenia dasyuri was found to have closest affinity with the family Paruterinidae, especially the genus Cladotaenia Cohn, 1901. The postulated theory of transfer of an ancestor of Anoplotaenia Beddard, 1911 transferring to the Tasmanian devil from an unrelated carnivorous host, such as an accipitriform or other carnivorous bird, is discussed and supported.
Enhanced Planar Perovskite Solar Cell Performance via Contact Passivation of TiO2/Perovskite Interface with NaCl Doping Approach

Perovskite solar cells (PSCs) have been developed rapidly in recent years due to the excellent photoelectric properties and development potential. In this study, high performance and hysteresis-less planar structured perovskite (MA1–yFAyPbI3–xClx) solar cell was successfully achieved via contact passivation of the compact titanium dioxide (TiO2)/perovskite interface with NaCl doping method. It was found that the sodium chloride (NaCl) doping treatment on TiO2 could significantly improve the electrical property of TiO2 electron transport layer (ETL), and passivate the trap states at TiO2/perovskite interface. Moreover, the improved interface contact between TiO2 ETL and perovskite could efficiently enhance the charge transfer and suppress the charge recombination in the device. Hence, the power conversion efficiency (PCE) of PSC device based on NaCl-doped TiO2 was enhanced to 18.3% compared with the pristine compact TiO2-based PSCs (15.1%).
Very-high-precision bound-state spectroscopy near a 85Rb Feshbaeh resonance

We precisely measured the binding energy ɛbind) of a molecular stale near the Feshbach resonance In a 85Rb Bose-Einstein condensate (BEC). Rapid magnetic-field pulses induced coherent atom-molecule oscillations in the BEC. We measured the oscillation frequency as a function B tield and fit the data TO a coupled-channel model. Our analysis constrained the Feshbach resonance position [155.041(18) Gl, width 10.71(2; G] and background scattering length [-443[3]a0] and yielded new values fur the Rb interaction parameters. These results improved our estimate lor the stability condition of an attractive BEC. We also found evidence for a mean-field shift to ɛbind.
Learning connectivity and higher-order interactions in radial distribution grids

To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact distribution flow equations and the so-called self-driven graph Volterra model, this paper puts forth a nonlinear topology identification algorithm, that is able to reveal both the edge connections as well as their higher-order interactions. Preliminary numerical tests using real data on a 47-bus distribution grid showcase the merits of the proposed scheme relative to existing alternatives.
Certified Data Removal from Machine Learning Models

Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to "remove" data from a machine-learning model? We study this problem by defining certified removal: a very strong theoretical guarantee that a model from which data is removed cannot be distinguished from a model that never observed the data to begin with. We develop a certified-removal mechanism for linear classifiers and empirically study learning settings in which this mechanism is practical.
Deep learning analyses of synthetic spectral libraries with an application to the Gaia-ESO database

In the era of stellar spectroscopic surveys, synthetic spectral libraries will form the basis for the derivation of the stellar parameters and chemical abundances. In this paper, four popular synthetic grids (INTRIGOSS, FERRE, AMBRE, and PHOENIX) are used in our deep learning prediction framework (StarNet), and compared in an application to optical spectra from the Gaia-ESO survey. The stellar parameters for temperature, surface gravity, metallicity, radial velocity, rotational velocity, and [{\alpha}/Fe] are determined simultaneously for FGK type dwarfs and giants. StarNet was modified to mitigate the differences in the sampling between the synthetic grids and the observed spectra, by augmenting the grids with realistic observational signatures, in an attempt to incorporate both modelling and statistical uncertainties as part of the training. When applied to spectra from the Gaia-ESO spectroscopic survey and the Gaia-ESO benchmark stars, the INTRIGOSS-trained StarNet showed the best results with the least scatter. Training with the FERRE synthetic grid produces similarly accurate predictions (followed closely by the AMBRE grid), but over a wider range in stellar parameters and spectroscopic wavelengths . In the future, improvements in the underlying physics that generates these synthetic grids will be necessary for consistent high precision stellar parameters and chemical abundances from machine learning and other sophisticated data analysis tools.
Competing at the Cybathlon Championship for Athletes With Disabilities: Long-Term Motor Imagery Brain-Computer Interface Training of a Tetraplegic Cybathlete

Background: The brain-computer interface (BCI) race at the Cybathlon championship for athletes with disabilities challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who is tetraplegic and has trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events. Methods: A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery) (left (L) and right (R) arm and feet (F)) as well as relax state (X). Three games paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon: BrainDriver. An evaluation of the pilot’s performance is presented for two Cybathlon competition training periods – spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition.Results: Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy >90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274s - 156s (255±24s to 191±14s mean±std), over 17 days (10 sessions) in 2019, and from 230s - 168s (214±14s to 181±4s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly.Conclusions: The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Stress, arousal level and fatigue, associated with the competition challenge and performance pressure resulting in cognitive state changes, were likely contributing factors to the nonstationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration: not registered
Sonovortex: Aerial Haptic Layer Rendering by Aerodynamic Vortex and Focused Ultrasound

In this paper, a method of rendering aerial haptics that uses an aerodynamic vortex and focused ultrasound is presented. Significant research has been conducted on haptic applications based on multiple phenomena such as magnetic and electric fields, focused ultrasound, and laser plasma. By combining multiple physical quantities; the resolution, distance, and magnitude of force are enhanced. To combine multiple tactile technologies, basic experiments on resolution and discrimination threshold are required. Separate user studies were conducted using aerodynamic and ultrasonic haptics. Moreover, the perception of their superposition, in addition to their resolution, was tested. Although these fields cause no direct interference, the system enables the simultaneous perception of the tactile feedback of both stimuli. The results of this study are expected to contribute to expanding the expression of aerial haptic displays based on several principles.
TOXICITY GAS SENSOR BASED ON NANO FEATURES GAN/PS PREPARED BY PULSED LASER DEPOSITION

GaN thin films were deposited on a porous silicon substrate (PS) by pulsed laser deposition method in the nitrogen atmosphere for using as ammonia gas sensor. Surface topography distinguished by atomic force microscopy (AFM) where the film appears homogeneous and the grain distribution forms are almost convergent (Roughness Average (nm)= 6.758, Surface Area Increase(nm)= 5.423, Average Grain Size (nm)= 16.49). A high density of the GaN nanostructure with shapes like-nano cauliflowers as shown in the field emission scanning electron microscope (FESEM). XRD measurement showed that GaN have a hexagonal structure. photoluminescence (PL) was performed at wavelengths (413.5 nm) and the emission spectrum was obtained by calculating the energy gap at these wavelengths. Knowing the intensity from the Raman spectrometer calculation. The gas sensitivity for NH3 gas were measured for fabricated gas sensor device as a function of concentration. Keyword: GaN thin films, Pulsed laser deposition, ammonia gas sensor, Photoluminescence
Robust continuous third-order finite time sliding mode controllers for exoskeleton robot

In this work, continuous third-order sliding mode controllers are presented to control a five degrees-of-freedom (5-DOF) exoskeleton robot. This latter is used in physiotherapy rehabilitation of upper extremities. The aspiration is to assist the movements of patients with severe motor limitations. The control objective is then to design adept controllers to follow desired trajectories smoothly and precisely. Accordingly, it is proposed, in this work, a class of homogeneous algorithms of sliding modes having finite-time convergence properties of the states. They provide continuous control signals and are robust regardless of non-modeled dynamics, uncertainties and external disturbances. A comparative study with a robust finite-time sliding mode controller proposed in literature is performed. Simulations are accomplished to investigate the efficacy of these algorithms and the obtained results are analyzed.
LOCOMOTION AND BODY CARE NEEDS ASSOCIATED WITH FUNCTIONAL DISABILITY IN OLDER ADULTS: ICNP© NURSING DIAGNOSES

HOW TO REFERENCE THIS ARTICLE: Morais YJGA, Silva DF da, Santos GCV, Brasil MHF, Gomes GLL, Oliveira FMRL de. Locomotion and body care needs associated with functional disability in older adults: ICNP© nursing diagnoses. Cogit. Enferm. [Internet]. 2021 [accessed “insert day, monh and year”]; 26. Available from: http://dx.doi. org/10.5380/ce.v26i0. 75913. ABSTRACT Objective: to identify the association of Nursing diagnoses related to locomotion and body care with functional disability in hospitalized older adults. Method: a descriptive and cross-sectional study conducted in 2019 with 100 older adults in a public hospital from Paraíba, Brazil. A semistructured instrument and the Barthel Index were used, with diagnoses being listed from the ICNP©. Descriptive statistics was used for data analysis. Results: total dependence was associated with Impaired ability to perform oral hygiene, Impaired ability to perform hygiene, and Impaired mobility in bed. Severe dependence was associated with Impaired ability to perform oral hygiene, Impaired ability to perform hygiene, Impaired ability to dress, Impaired mobility, and Impaired mobility in bed. Conclusion: the diagnoses identified provide support for planning and implementing Nursing care aimed at shorter hospitalization times, preventing unfavorable health outcomes and ensuring comprehensive and individualized care for the population.
Role Of Magnetic Resonance Imaging in The Assessment of Operated Brain Glioblastoma: Outcome After Therapy

Background: Glioblastoma Multiforme (GBM) is a grade IV glial brain tumor that is characterized by poorly differentiated anaplastic cells surrounding focal areas of necrosis and neovascularity. Objective: To identify role of Magnetic Resonance Imaging (MRI) in assessment of glioblastoma response to treatment Methods: This a prospective cohort study included 12 patients (4 males and 8 females) their ages ranged from 25 years to 69 (53.6 ± 15.1 years). patients with pathologically proved GBM grade IV (GBM) who were operated and received both radiotherapy and chemotherapy were enrolled to this study. MRI was done within 48 hours and 3 months postoperative. Imaging modalities applied in both MRI exams were: Conventional Magnetic Resonance imaging, MRI Diffusion weighted images, and Contrast imaging. Results: There was statistically significant reduction in edema, mass effect, T2 and FLAIR signals 3 months postoperatively in comparison to 48 hours MRI findings. Post-operative hemorrhagic signal reduced in follow up after 3 months but with no significant difference. Marginal enhancement in the immediate post-operative were detected in all cases, however, in the 3 months follow up was limited to 10 cases only with 8 of them also showed recurrent solid measurable component, DWI marginal restriction was the finding in the immediate 48h in 8 cases mostly related to surgical interference ischemia, however in the 3months follow up 6 of the measurable enhanced lesions displayed diffusion restriction and two of the measurable recurrent masses had not definite restriction. Conclusion: Immediate post operative MRI is a gold base line to discriminate the post operative sequel and recognize the different surgery related finding and for proper interpretation of therapy related response in the subsequent follow up imaging which intern could avoid unnecessary biopsies in follow up.
Design of IoT-based System for Smart Temporary Waste Shelter

Internet of Things (IoT) is a concept where all products can interact with each other to help human activities by utilizing the internet. The IoT system can help solve problems such as the schedule-based retrieval system at a temporary shelter in Indonesia. This paper creates a web-based geographic information system along with the IoT-based waste temporary shelter prototype. The prototype also integrated with an Arduino Microcontroller so that it can sort the waste automatically into three types of waste, then record data in the form of height and weight, and send the results of the data to the database so that the geographic information system can display the results of the data. Node-RED serves as an Application Programming Interface (API) that sends data from the server to the database belonging to a web-based geographic information system. The results of the performance analysis are that the prototype has sorted waste well, and the system also produced good QoS by ETSI standards when looking at the network traffic from the MQTT server to the database, and from the end-user to the web. As for the QoE results for tools and systems have produced good results according to the ITU-T standard.
Design and Implementation of E-Commerce Platform Based on Android

This paper introduces the basic functions of the e-commerce system implemented on Android, including user management functions, product search functions, product browsing functions, and product tracking functions, etc. It is necessary to use technologies in Android in order to implement each module function. For example, network communication requires Android’s network request technology and data analysis technology. The display of pictures requires the use of Android controls and cache technology. The traceability of products requires camera scanning technology. And RSA decryption technology and so on. Through the above thread pool technology and the use of caching mechanism, the user experience of UI will be improved and unnecessary network resources consumption will be avoided.
A LOW-NOISE HIGH-PERFORMANCE INSTRUMENTATION AMPLIFIER BASED CNFET READOUT CIRCUIT FOR CARDIAC TROPONIN I DETECTION

Detection of cardiac biomarker is crucial in diagnosis of acute myocardial infraction (AMI). According to National Centre for Biotechnology Information (NCBI) increase in Troponin protein beyond the limits in the blood is main cause for cardiac injury. Researchers have designed and implemented precise and accurate Troponin I detection sensors. Output of these sensors is too low to activate read out device for display. Therefore, too low output should be strengthened and further processed by signal conditioning circuit. This Paper proposes design of first block Instrumentation amplifier of signal conditioning circuit using Carbon Nanotube Field Effect Transistors (CNFETs). Proposed instrumentation amplifier designed with CNFETs has very low noise and high performance of 108dB gain with a signal to noise ratio of 116dB. Sensor output is too low in the range of microvolts which is prone to low frequency noise. Proposed circuit reduces noise density to 0.4pV/√Hz.
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities

In this extended abstract, we will present and discuss opportunities and challenges brought about by a new deep learning method by AUC maximization (aka \underline{\bf D}eep \underline{\bf A}UC \underline{\bf M}aximization or {\bf DAM}) for medical image classification. Since AUC (aka area under ROC curve) is a standard performance measure for medical image classification, hence directly optimizing AUC could achieve a better performance for learning a deep neural network than minimizing a traditional loss function (e.g., cross-entropy loss). Recently, there emerges a trend of using deep AUC maximization for large-scale medical image classification. In this paper, we will discuss these recent results by highlighting (i) the advancements brought by stochastic non-convex optimization algorithms for DAM; (ii) the promising results on various medical image classification problems. Then, we will discuss challenges and opportunities of DAM for medical image classification from three perspectives, feature learning, large-scale optimization, and learning trustworthy AI models.
Allele mining for the grain number gene An-1 in rice (Oryza sativa L.)

Rice yield has attained a plateau and hence the enhancement of grain yield is indispensable to feed the growing population, which could be achieved by the identification of superior alleles in the existing germplasm. Any variation in the pleiotropic gene, An-1 (yield gene) leads to enhanced grain number and grain size in rice. Hence, the gene was chosen for analyzing the allelic diversity/haplotype variation with 150 lines of 3K RG panel which revealed that, the gene An-1 has 20 Single Nucleotide Polymorphisms and 10 INDELs encompassing both intronic and exonic regions. The genotypes were divided into four haplotypes in the combination of seven SNPs with the maximum number of genotypes in the first haplotype and the least number of genotypes in fourth haplotype. From the study, H1 was identified as a superior haplotype. The haplo-pheno analysis identified the superior donors viz., SIGARDIS, GENIT and DAMNOEUB KAUN KHMOM harbouring superior haplotype combinations, which may be further utilized in haplotype-based breeding for the development of high yielding rice varieties.