Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Accurate Image Matching by Exploring Redundancy Between Multiple Descriptors

Finding correspondences between a pair of images is the key ingredient for many applications such as localization and panorama. However, due to a variety of challenges between multiview images in practice, the results of using a single kind of descriptor may vary significantly across different scenes. In this letter, we treat the assignment task as a clustering problem and propose an image matching method that fuses multiple descriptors to tackle the above difficulties. First, we extract multiple descriptors at the keypoints on two images. Then, we compute a pairwise similarity matrix for each kind of descriptor. Afterward, we compute a weighted combination of these similarity matrices, and use it to build correspondences via a modified multikernel clustering module. The proposed method is tested on three public image datasets: two ground image sets and an unmanned aerial vehicle (UAV) image set. Experiments show that the proposed method can adapt to different number of descriptors. It significantly improves the matching accuracy in a variety of scenarios and downstream tasks.
A study on the investors’ intentions in Non-Fungible Tokens through the lens of Technology-Readiness-Index Theory

This study explores the influence of the investors’ personality dimensions on the Non-Fungible Tokens (NFT) technology readiness index (TRI) and financial instrument-specific factors of technology acceptance. The survey comprised of 197 respondents. The responses-data were tested on the conceptual model using SEM (structural equation modeling). TRI 2.0 explored customers’ predispositions towards NFT investments. Mental enablers and inhibitors explained NFT perceived value and NFT perceived trust. The research found that the enabler of TRI (innovativeness) and inhibitors of TRI (insecurity and discomfort) along with perceived trust significantly affect the NFT investment intention It was also found that optimism and perceived value for NFT did not significantly impact NFT investment intentions. These research results shall facilitate government, NFT marketplaces, brands, and private organizations to prioritize TRI enablers and decrease inhibitors to ensure successful NFT investments.
Individual Pitch Control Based on Local and Upstream Inflow Measurements

As wind turbine sizes are increasing, the issue of load alleviation is becoming increasingly important to accommodate design requirements. Load alleviation can be achieved using either local flow devices, such as trailing edge flaps or using individual pitch, the latter being the topic of the present study. In this study, a controller based entirely on inflow measurements is designed. The performance of the controller is tested through simulations in standard operating conditions using two different measurement types; local measurements of angle of attack and relative velocity at a radial position on each blade, and upwind inflow measurements from a spinner mounted LIDAR. The controller is tested both with and without the ability to preview the incoming wind speed. Results show that using either of the tested control strategies the 1P peak in the load spectrum can be removed. It is shown that fatigue load reductions of around 30 percent can be achieved using the local inflow measurements and around 20 percent using the spinner LIDAR measurements.
Trajectory Tracking Control of an Underactuated Underwater Vehicle Redundant Manipulator System

The purpose of this study is to control the position of an underactuated underwater vehicle manipulator system (U‐UVMS). It is possible to control the end‐effector using a regular 6‐DOF manipulator despite the undesired displacements of the underactuated vehicle within a certain range. However, in this study an 8‐DOF redundant manipulator is used in order to increase the positioning accuracy of the end‐effector. The redundancy is resolved according to the criterion of minimal vehicle and joint motions. The underactuated underwater vehicle redundant manipulator system is modeled including the hydrodynamic forces for the manipulator in addition to those for the autonomous underwater vehicle (AUV). The shadowing effects of the bodies on each other are also taken into account when computing the hydrodynamic forces. The Newton‐Euler formulation is used to derive the system equations of motion including the thruster dynamics. In order to establish the end‐effector trajectory tracking control of the system, an inverse dynamics control law is formulated. The effectiveness of the control law even in the presence of parameter uncertainties and disturbing ocean currents is illustrated by simulations.
Multichannel Acoustic Echo Canceller Based on Fast Affine Projection Algorithm

The task of the identification of the multichannel linear system with long channel impulse responses is considered in this paper. The multichannel acoustic echo cancellation in the closed rooms is an example of this task. Dependently on the room size, its decoration and the signal sampling frequency value the duration of the identified impulse responses may take a few thousand samples. To solve this task a multichannel adaptive filter with the corresponding large number of weights is required. The implementation of such filters is a difficult task as the arithmetic complexity of the adaptive filters depends on the number of the filter weights. Due to this reason the effective time-domain Recursive Least Squares (RLS) adaptive filters even in their fast (computationally efficient) forms are rarely used in the acoustic echo cancellers. The computationally simple but less effective time-domain Least Means Squares (LMS) or the Normalized LMS (NLMS) adaptive filters are usually used for this purpose. However, such filters have a number of well-known drawbacks. The slow convergence is one of them when the correlated or nonstationary signals are processed. Speech is an example of such signals. The frequency-domain LMS/NLMS adaptive filters de-correlate the signals. These filters have a smaller computational complexity comparing to their time-domain prototypes. However, as the signals are processed using the blocks of the samples, the frequency-domain adaptive filters introduce the output signal delay and demonstrate a slower convergence because their weights are updated only once per block. This decreases the tracking ability of the frequency-domain adaptive filters. The ability is an important feature of the adaptive filters in the acoustic echo cancellation task because the acoustic medium is usually a not time-invariant one. The usage of the Fast Affine Projection (FAP) adaptive filter is the tradeoff between the acoustic echo canceller efficiency and its complexity. Such adaptive filter is widely used in the single-channel acoustic echo cancellers. This paper considers the adaptive filter application in the multichannel echo cancellers. The computational procedure of a multichannel FAP adaptive filter is presented and an example of the two-channel echo canceller is considered in this paper. The simulation demonstrates about the same steady-state performance of the FAP adaptive filter as that of the time-domain RLS adaptive filter or the time/frequency domain NLMS adaptive filter. However the convergence of the FAP adaptive filter comparing to the NLMS one is a few times faster. Because of this feature the FAP adaptive filter can efficiently track the changes in the processed signal statistics and/or the changes in the acoustic medium.
Cache Update and Delivery of Dynamic Contents: A Stochastic Game Approach

In this paper, we propose a cache update policy for wireless networks considering dynamic popularity for file requests. Our network scenario is a wireless network comprised of some cache-equipped access points (APs) which are deployed densely in an area and have connections with core network servers. We model the dynamics of file requests as Markov-modulated Poisson processes. Considering the congestion-dependent delay of APs’ services and their overlapping coverage areas, we model the cache service for the cache-missed requests, as a stochastic game in which a Vickrey–Clarke–Groves (VCG) mechanism is exploited at each stage of the game as our proposed file routing policy, and the APs decide independently how to update their caches regarding the routed files. To this end, the APs’ utilities are defined based on long-term average file delivery delay including the queueing delay in APs. By finding the Nash equilibrium of the game, we propose policies for both delivery and cache update of dynamic contents. Finally, by comparing numerical results of our proposed scheme with some conventional caching schemes, we show significant improvements in network performance in terms of file delivery delay in different conditions.
A Method of Anti-Disturbance by Ragged Clouds for Detecting Ships on the Sea

Focusing on the problem of ship detection in poor imaging conditions,which means here,being disturbed by lots of ragged clouds in optical images,a robust ship detection algorithm is proposed based on the characteristics of the background and the target itself.Firstly,it extracts ROI using two parameters and local CFAR.Then it extracts the wake information by the improved local Radon transform and a peak value extraction algorithm in succession.The experiments of satellite images show that this method can reduce false alarms caused by ragged clouds effectively.
Atrial Fibrillation Monitoring Based on Noncontact Capacitive ECG Using an Integrated Microhumidity Fabric Electrode-Sheet Sensing Scheme

This study designed a novel noncontact capacitive electrocardiogram (cECG) system for long-term atrial fibrillation (AF) monitoring. Unlike conventional ECG monitoring, the proposed system can record ECG signals without touching a subject’s skin, avoiding the problem of skin allergy by using wet electrodes. The main contributions of this study include the two aspects. First, a noncontact electrode based on a microhumidity fabric structure is proposed, which can improve the noncontact cECG signal quality. Second, based on the proposed electrode, the possibility of its application in long-term AF monitoring was investigated. The microhumidity fabric electrode can improve the microhumidity of the skin-electrode interface and the noncontact cECG signal quality. This humidity is low and can hardly be felt by the human body, which can also ensure comfort. The experimental results show that compared with the conventional noncontact fabric electrode, the skin-electrode impedance decreases from 104.1 ± 58.1 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> (when using the electrode without the microhumidity structure) at 0.5 Hz to 2.8 ± 1.79 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> (with the microhumidity structure). In the long-term cECG recording experiment, the system has high consistency with the reference equipment. The RR (R-wave to R-wave) interval correlation coefficient is 0.998, and the maximum error <inline-formula> <tex-math notation="LaTeX">$\le 18$ </tex-math></inline-formula> ms. For the validation of AF monitoring, a designed AF analysis algorithm is integrated into the noncontact cECG system, including effective signal screening and AF recognition algorithm based on RR interval and support vector machine (SVM), with an average accuracy of 98.57% on the wearable database. The algorithm is verified on three healthy people and one paroxysmal AF (PAF) patient using the noncontact cECG system with an average accuracy of 98.12%, showing that our system has the potential for long-term AF monitoring.
Development of a Multi-Epitope Universal mRNA Vaccine Candidate for Monkeypox, Smallpox, and Vaccinia Viruses: Design and In Silico Analyses

Notwithstanding the presence of a smallpox vaccine that is effective against monkeypox (mpox), developing a universal vaccine candidate against monkeypox virus (MPXV) is highly required as the mpox multi-country outbreak has increased global concern. MPXV, along with variola virus (VARV) and vaccinia virus (VACV), belongs to the Orthopoxvirus genus. Due to the genetic similarity of antigens in this study, we have designed a potentially universal mRNA vaccine based on conserved epitopes that are specific to these three viruses. In order to design a potentially universal mRNA vaccine, antigens A29, A30, A35, B6, and M1 were selected. The conserved sequences among the three viral species—MPXV, VACV, and VARV—were detected, and B and T cell epitopes containing the conserved elements were used for the design of the multi-epitope mRNA construct. Immunoinformatics analyses demonstrated the stability of the vaccine construct and optimal binding to MHC molecules. Humoral and cellular immune responses were induced by immune simulation analyses. Eventually, based on in silico analysis, the universal mRNA multi-epitope vaccine candidate designed in this study may have a potential protection against MPXV, VARV, and VACV that will contribute to the advancement of prevention strategies for unpredictable pandemics.
Integration of EEG and Eye Tracking Technology: A Systematic Review

Electroencephalography, or EEG, measures brain signals and activities using small electrodes which read electrical activity in the brain. For understanding cognitive patterns and diagnosing any abnormalities in the brain, this is a widely used test. Eye-tracking, on the other side, is the measurement of gaze or eye movements which is being utilized in research and investigation to decode human behaviors. Combining the features of EEG and eye-tracking activities would provide helpful insight into understanding a human's cognitive behavioral process. An array of highly sophisticated experiments was conducted with the combination of both features. This paper will traverse previous studies and research integrating EEG and eye-tracking and prepare a taxonomy of the implementations in several prominent fields. Further, it unravels the current challenges and limitations of working in these areas and proposes enhancements to be considered in future research. This review work will assist as a consensus of the current state of knowledge in the future new research directions in exploring the integration of EEG and eye-tracking technology.
Topological optimization for placement of damping material on cylindrical shells

On the basis of topological evolutionary structural method(ESM) and taking the maximum of modal loss factor as target function,the needed volume of damping material as constraint condition,and the damping element as design variable,the topological optimization for placement of constraint damping material and free damping material on cylindrical shell was studied.The sensitivity of damping element location on modal loss factor was analysed.In the light of topological ESM,gradually deleting the material of low utilization ratio and making modal loss factor maximal,a numerical example was completed,which indicates the validity and feasibility of the proposed method.
Inégalités matricielles quadratiques et stabilité des polynômes

New relations are established between various matrix inequalities for testing stability of a scalar polynomial. These inequalities are quadratic functions of the coefficients of the polynomial, and linear (LMI) in an additional matrix. Links are established between Hermite and Lyapunov stability criteria, and between state-space and polynomial techniques for stability analysis. These conditions may be used to design reduced-complexity control laws.
EEG based BCI for Autonomous Control: A Review

Brain-computer interface (BCI) has had exciting improvements in the last decade due to the developments in brain science and computer technology, making BCI a top research area in applied science. With the advancement of technologies, researchers are now experimenting using BCI for operating vehicles, robots, and aircraft. However, choosing the suitable brain signal acquisition technique among the available ones is a big concern. In this paper, we will survey the studies that used the BCI framework for controlling vehicles and quadcopters, its limitations, its prospect, and the reason electroencephalogram (EEG) best suits the BCI framework.
Switching Speed Analysis of MOSFET-Based Absorber Integrated Antenna for 5G/WiMAX/WLAN

A block-by-block performance analysis of a novel Metal Oxide Semiconductor Field Effect Transistor (MOSFET)-based absorber antenna has been presented. The proposed integrated antenna is a solution to the negative effect of impedance mismatch between the power amplifier and antenna in the front end of communication systems. The clockwise diode base quasi circulator device used in the proposed design has been observed to have an insertion loss (S21 and S32) of 7 dB and 9.2 dB, respectively, with a return loss (S22) of 2.8 dB. The MOSFET-based absorber presented an insertion loss, return loss, and absorption efficiency of 2.75 dB, 3.3 dB, and > 90%, respectively. The switching speed for the proposed MOSFET-based absorber antenna model has been observed as 0.15 ns.
Crowdsourcing Affective Annotations Via fNIRS-BCI

Affective annotation refers to the process of labeling media content based on the emotions they evoke. Since such experiences are inherently subjective and depend on individual differences, the central challenge is associating digital content with its affective, interindividual experience. Here, we present a first-of-its-kind methodology for affective annotation directly from brain signals by monitoring the affective experience of a crowd of individuals via functional near-infrared spectroscopy (fNIRS). An experiment is reported in which fNIRS was recorded from 31 participants to develop a brain-computer interface (BCI) for affective annotation. Brain signals evoked by images were used to draw predictions about the affective dimensions that characterize the stimuli. By combining annotations, the results show that monitoring crowd responses can draw accurate affective annotations, with performance improving significantly with increases in crowd size. Our methodology demonstrates a proof-of-concept to source affective annotations from a crowd of BCI users without requiring any auxiliary mental or physical interaction.
Twitter Sentiment Analysis-Based Adjustment of Cryptocurrency Action Recommendation Model for Profit Maximization

Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as “Sell”, “Buy”, and “Wait. ” In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.
Modeling of poverty level in South Sulawesi Province through spline nonparametric regression approach

: This study discussed modeling poverty levels in South Sulawesi through Spline Nonparametric Regression Approach. Since no specific pattern was formed from the relation between the poverty level in South Sulawesi and the factors that influence it, the researchers used nonparametric regression modelling with Spline approach. The Spline model is very good in modelling data that has changeable patterns at certain subintervals. The aimed of this study were to investigate the factors that have the most influence on the poverty level and modelling the poverty level in South Sulawesi through nonparametric regression. The scope of this study was the use of Generalized Cross-Validation (GCV) in selecting optimal knot points; in this case, it used 1, 2
Atomistic Insights Into the Regulatory Mechanisms Mediated by Post- Translational Modifications: Molecular Dynamics Investigations

Post-translational phosphorylation is a ubiquitous mechanism for cellular regulation, playing a crucial role in a diverse array of processes. At the molecular level, the phosphorylation seems to cause electrostatic perturbations which modulate the energy landscape governing protein folding, activity, protein-protein interactions and, conformational dynamics. However, details on structural effects induced by phosphorylation of proteins are still poorly understood. In this context, computational simulations recently provided a valuable alternative in the elucidation of principles and mechanisms of protein phosphorylation. In the present contribution, we review several recent studies devoted to molecular dynamics simulations of phospho-proteins with particular attention to proteins involved in the p53 pathway, ubiquitin pathway, protein kinases and intrinsically disordered proteins. Since application of biomolecular simulations to the investigation of mechanisms related to protein phospho-regulation is a newborn field for computational biology, our contribution could provide a suitable framework to plan future researches and to rationalize the available data in the field.
The Diagnosis of “Pervasive Developmental Disorder Not Otherwise Specified”: A Systematic Literature Review

(1) Background: The study deepens the diagnosis of “pervasive developmental disorder not otherwise specified” (PDD-NOS), a subthreshold diagnosis introduced in DSM-IV and then absorbed into the broader spectrum disorder of autism in DSM 5. The presence of people still attributed with a diagnosis of PDD-NOS can cause confusion in the understanding of this disorder, which is no longer present in the current diagnostic system. This review aims to gain a deeper understanding of the characteristics and boundaries of diagnosis, its use within the scientific community, and the long-term stability of that diagnosis. (2) Methods: The Prisma method was used to carry out the literature review; the scientific papers were selected using scientific search engines, including SCOPUS, PUBMED, and PsychINFO. Twenty-three articles were finally selected, and a meticulous reading was carried out in relation to the research questions. (3) Results: Four cross-cutting categories were identified: (1) diagnosis; (2) differential diagnosis; (3) prognosis; and (4) comorbidity. (4) Conclusions: Limits have emerged regarding the consistency, sensitivity, and the stability of PDD-NOS. The inclusion of this diagnosis within the broader autism spectrum disorder category coined in DSM-5 appears applicable.