Miracle-dz/UniFAR-FTUs huggingface dataset:
Cone-Beam Computed Tomography - A Useful Tool in Orthodontic Diagnosis

In order to accurately make a diagnosis, it is necessary to introduce into the current practice of innovative technologies. The aim of this study was to provide a comprehensive overview of the use of the cone-beam computed tomography (CBCT) image, the benefits of its application in orthodontics. This retrospective study was conducted on 405 patients, aged 6-30 years, who required orthodontic treatment. CBCT was indicated in 42 (10.37%) patients. The CBCT analyses were conducted using a PaX-i3D Green - 3D Imaging Systems – VATECH and images were analysed using EZ 3D Plus Professional Ver.1. 2.6. 23 software (VATECH). After analysing the 42 CBCTs, we diagnosed: impacted teeth on 17 CBCTs, supernumerary teeth on 4 CBCTs and transposed teeth in 2 CBCTs. Pathological root resorptions was identified on 5 CBCTs, appreciation of the periodontium on 15 CBCTs, cleft lip and palate on 3 CBCTs, orthodontic-surgical protocol on 2 CBCTs, pathology of TMJ on 7 CBCTs, and assessment of the upper airways on 2 CBCTs. Thus on 24 CBCTs we were able to gather information on several pathologies. The information obtained from CBCT imaging allows accurate measurements, location of impacted teeth, ensures visualization of respiratory anomalies, identifies and quantifies skeletal asymmetry.
A Multiclass EEG Signal Classification Model using Spatial Feature Extraction and XGBoost Algorithm

Brain-computer interface is a framework which provides a communicating pathway between the human brain and neuroprosthetic devices. In this work, we have performed signal smoothing to fix unregulated electroencephalogram (EEG) fluctuations and to effectively collect hidden patterns in corresponding EEG spectra. To do it, we have applied a Savitzky Golay function because of its ability to preserve spectral properties without distorting it much. For feature selection, we have applied the Filter Bank Common Spatial Pattern (FBCSP) algorithm with the Principle Component Analysis (PCA). FBCSP creates 11352 features from EEG signals and PCA gradually reduces the features to 185 most significant features. These features are utilized in the classification process by the eXtreme Gradient Boosting (XGBoost) algorithm with suitable node split criteria to manage optimal tree height. A five-fold cross-validation approach concludes the superior performance of XGBoost in terms of minimizing execution time (3.7 times faster in the training phase) and providing improved accuracy as compared to existing results. Our approach enhances classification accuracy (88.80%) by approximately 10% over regularized common spatial patterns (78.01% accuracy), 15% over shift variance approximation (73.84% accuracy) and 15% over Riemannian approach (74.77% accuracy). It also concludes that the pre-consideration of the noise level in EEG spectra provides a better approximation.
Feasibility of Gait Entrainment to Hip Mechanical Perturbation for Locomotor Rehabilitation*

While rehabilitation of upper-limb motor function with human-interactive robots has been met with success, robot-aided locomotor rehabilitation has proven challenging. To inform more effective approaches to robotic gait therapy, it is important to understand neuro-mechanical dynamics and control of unimpaired locomotion. Our previous studies reported that human gait entrained to periodic mechanical perturbations at the ankle when the perturbation period was close to preferred walking cadence. Moreover, entrainment was accompanied by synchronizing the perturbations to a constant gait phase, the same for all subjects, where they provided mechanical assistance. To test the generality of entrainment-based assistance, the present study evaluated the behavior of five unimpaired subjects who walked overground while wearing a hip exoskeleton robot. Periodic torque pulses were applied to the subjects’ hips, with a period different from, but close to, their preferred stride cadence. Results indicated that unimpaired subjects entrained their gait to periodic mechanical perturbations at the hip. Convergence of relative phase between gait and perturbations was observed, but clustered around two distinct gait phases, in contrast to the single converged phase observed in entrainment to periodic ankle torques. These entrainment studies quantify important aspects of the nonlinear neuro-mechanical dynamics underlying the control of walking, which will inform the development of effective approaches to robotic walking therapy.
$H_\infty$ Control for a Class of Discrete-Time Systems via Data-Based Policy Iteration With Application to Wind Turbine Control

This paper concerns a novel data-based policy iteration control method for discrete-time systems with disturbances. To avoid the need of system dynamic and state, a new system equation is constructed by using system operation data. Based on this, we derive a new data-based Bellman equation that can be applied to the design of model-free policy iteration algorithm. For discrete-time system with disturbances, we transform $H_\infty $ control into a zero-sum game problem, and then use the proposed new data-based PI method to solve it. Wind turbine control is a typical control problem with time delay and disturbance. Mathematical simulation results are given to demonstrate the effectiveness of the proposed data-based PI control method.
Whole-Body Control of Humanoid Robot in 3D Multi-Contact under Contact Wrench Constraints Including Joint Load Reduction with Self-Collision and Internal Wrench Distribution

In this paper, we propose an approach for online whole-body control of position-controlled humanoid robot with 3D multi-contact to cope with contact wrench constraints and joint overload. In our method, robots are controlled under contact wrench constraints with three features: 1) internal wrench control to reduce joint load and prolong the time in which the high-load postures can be maintained 2) feasible utilization of self-collision to reduce joint load by turning off joint servo gains 3) handling degenerated degree of freedom by solving a quadratic optimization problem integrating wrench distribution and inverse kinematics in which internal wrench is controlled only in controllable directions.With our methods, HRP2-JSKNTS could pick up an object under a desk with squatting with the back of the upper leg on the back of the lower leg without sliding at the right arm. We also evaluated the effectiveness of our control to reduce joint load with another experiment.
Performance Guarantees for Receding Horizon Search with Terminal Cost

We present a novel method of using terminal costs in the construction of a receding horizon search path. We prove that the proposed method of constructing search paths provides a theoretical lower bound on the performance of the search path. Our result can be interpreted as ensuring that the receding horizon path performs no worse in expectation than a given sub-optimal search path. This result is especially practical for subsea applications where, due to use of side-scan sonar in search applications, search paths typically consist of parallel straight lines. Thus for subsea search applications, our approach ensures that expected performance is no worse than the usual subsea search path, and it might be much better. We demonstrate the efficacy of the proposed method by planning search paths in simulation using real-world data that was acquired by an autonomous underwater vehicle during a subsea survey of Boston Harbor.
Learning Driving Models From Parallel End-to-End Driving Data Set

Parallel end-to-end driving aims to improve the performance of end-to-end driving models using both simulated- and real-world data. However, how to efficiently utilize the data from both the simulated world and the real world remains a difficult issue, since these data are usually not well aligned. In this article, we build a data set called the parallel end-to-end driving data set (PED) for parallel end-to-end driving research. PED consists of 13 000 images from the simulated world and 13 000 images from the real world that are used to train the model, as well as 2700 images from the real world that are used to test the model. The simulated-world data in PED are constructed according to the real world, and each simulated-world image corresponds to a real-world image. PED also contains the vehicle measurement data (GPS, speed, steering angle, and heading direction of the vehicle) related to both the simulated- and real-world images, which are not available in some other data sets. We conduct two types of experiments to illustrate the effectiveness and the superiority of PED and explore some ways to mix the simulated-world data with the real-world data to improve the performance of end-to-end driving models.
Detection of Specific Language Impairment in Children Using Glottal Source Features

Developmental dysphasia, also known as specific language impairment (SLI), is a language disorder in children that involves difficulty in speaking and understanding spoken words. Detecting SLI at an early stage is very important for successful speech therapy in children. In this paper, we propose a novel approach based on glottal source features for detecting children with SLI using the speech signal. The proposed method utilizes time- and frequency-domain glottal parameters, which are extracted from the voice source signal obtained using glottal inverse filtering (GIF). In addition, Mel-frequency cepstral coefficient (MFCC) and openSMILE based acoustic features are also extracted from speech utterances. Two machine learning algorithms, namely, support vector machine (SVM) and feed-forward neural network (FFNN), are trained separately for the MFCC, openSMILE and glottal features. A leave-fourteen-speakers-out cross-validation strategy is used for evaluating the classifiers. The experiments are conducted using the SLI speech corpus launched by the LANNA research group. Experimental results show that the glottal parameters contain significant discriminative information required for identifying children with SLI. Furthermore, the complementary nature of glottal parameters is investigated by independently combining these features with the MFCC and openSMILE acoustic features. The overall results indicate that the glottal features when used in combination with MFCC feature set provides the best performance with the FFNN classifier in the speaker-independent scenario.
Average Capacity Analysis and Power Splitting with Direct Link Based Relaying Protocol for Wireless Information and Power Transfer Networks

This paper investigates the power splitting based relaying protocol for simultaneous wireless information and power transfer (SWIPT) in a three-node communication network with a direct connection path. The energy harvesting of the relay node depends on source transmissions. The performance of the average capacity of the proposed model is studied. The capacity expression of the destination of the power splitting-based relaying (PSR) protocol of the direct link is obtained. We find the average capacity of the system maximization depends on system para-meters. An amplify-and-forward relay is used in researching the average capacity performance of the PSR protocol. We verify all the analysis results by numerical simulation, and prove that the proposed PSR protocol with direct link is better than the existing power splitting protocol non-direct link.
Preliminary Evaluation of an Orbital Camera for Teleoperation of Remote Manipulators

Conventional teleoperative interfaces, used for remote manipulation in the nuclear industry, utilise multiple stationary CCTV cameras for situational awareness. This is cognitively challenging for the human operator, who must mentally combine information from multiple 2D views, before attempting 3D reasoning about the remote environment. To enhance performance on telemanipulation tasks, this paper explores the merits of employing an orbital camera view. The orbital camera’s gaze direction is automatically fixated towards the robot’s moving end-effector. Meanwhile, the camera can be controlled by the operator to move on a spherical surface around the end-effector, in addition to zooming capability. The robot’s Cartesian motion coordinates are also continuously transformed to be controllable with respect to the axes of the orbital camera view. Human test-subject experiments were conducted in a simulation environment using novice robot operators, and a variety of metrics were collected. Although the results do not show an objective difference between the camera modes, participant interviews suggest the orbital camera is the preferred method for visual feedback with its dynamic views helping to overcome positioning difficulties associated with stationary cameras. The experiment also revealed significant confounding factors that contributed to the results being inconclusive. These are discussed and recommendations are made for future empirical experiments to evaluate such systems.
Transfer Learning for Cross-Model Regression in Performance Modeling for the Cloud

Performance characteristics of a complex system in different configurations are expensive to obtain due to the cost of sampling the system performance. We introduce ModelMap, a novel transfer learning technique for the performance modeling of configurable systems. ModelMap captures many explicit and latent types of dynamic system evolution, including configuration changes, scaling and hardware upgrades, by deriving and modeling directly these kinds of incremental transformations between system and/or application instances, over time. Modeling these transformations allows us to build accurate models for new configuration instances with just a few samples, and to interpolate across legacy models to build new models with no new samples at all. We experimentally test our method on a variety of system performance modeling and optimization scenarios. Compared to using conventional direct and incremental modeling techniques, our method achieves higher accuracy by up to an order of magnitude when the sampling budget is extremely limited, in particular between 0% to 5% of an exhaustive sampling budget. We also show how our method can be used to quickly derive an accurate resource allocation split that optimizes a given overall performance goal for co-hosted applications in a virtualized environment.
New Results on Testing Against Independence with Rate-Limited Constraints

This work studies error exponent limits in hypothesis testing (HT) in a distributed scenario with partial communication constraints. We derive general conditions on the Type I error restriction under which the error exponent of the optimal Type II error has a closed-form characterization for the task of testing against independence. We show that the error exponent is preserved for a family of decreasing Type I error restrictions. Complementing this analysis, new expressions are derived to bound the optimal Type II error probability for a finite number of observations. These bounds shed light about the velocity at which error exponent limits are attained with the number of samples.
Discriminating between Color Exposure and Idle State using EEG Signals for BCI Application

Brain-Computer Interfaces are systems that would allow either the reintegration of handicapped people or effective control of devices by thoughts for healthy people. Several neuroparadigms (motor imagery, SSVEPs, P300, SCP among others) have been explored to control a BCI, but passive color exposure is one of the most novels. However, the analysis of the potential of this task for being used in a self-paced BCI has not been studied enough. In this paper, the first step towards a self-paced color-exposure-based BCI is presented. This is done by the analysis of the differences between the interest activities (RGB colors) and idle state. Specifically, we extract EMD (Empirical Mode Decomposition)-based features from the EEG signals and then these are classified using 4 classifiers (SVM, random forest, Naive Bayes and KNN). We then present an analysis of the performances for these two classes (RGB colors vs idle state) obtained for seven subjects, highlighting SVM’s average performance (93.27% for all of them). Also, the differences between the two classes (color exposure and idle state) suggest that they do not depend on a specific subject. Last, we provide experimental evidence that there is no a specific color that could be distinguished better regardless idle state, reaching average accuracies of 85.01%, 85.56%, and 86.91% for red, green and blue vs idle state, respectively.
Proposal of Procedure for Heart Rate Variability Monitoring in Oncologic Patients Using a New Technology

Heart rate variability has become an important marker for prognosis not only in cardiovascular, diabetic or renal patients, but also in oncological patients. Heart rate variability as an indicator of autonomic nervous system dysfunction is associated with cancer progression, cancer-related-pain intensity, cancer-related fatigue and even quality of life. Moreover, heart rate variability is proposed as a marker for survival in oncological patients. Usually heart rate variability is determined by ECG Holter or ECG monitoring. These explorations are reliable, validated, but they are time-consuming, expensive, complex and the medical staff who will use them needs proper training. A new, easy, non-invasive, cost-effective method is wrist-worn photoplethysmography PulseOn. PuleOn was already been compared with ECG Holter in healthy individuals and the heart rate variability indicators were similar in both measurements. Confirming the validity of a simpler method for determining HRV in oncological patients will increase the quality of care of oncological patients. Also will increase the quality of their monitoring in terms of tumor progression, cancer-related-pain intensity, chemotherapy toxicity without costly explorations. The high ability of wrist-worn pulse wave to transmit the data from PulseOn to a dispositive with Android OS makes PulseOn a simple tool in telemonitoring oncological patients.
Deep Supervised Hashing with Similar Hierarchy for Place Recognition

Place recognition as one of the most significant requirements for long-term simultaneous localization and mapping (SLAM) has been developed rapidly in recent years. Also, deep learning is proved to be more capable than traditional methods to extract features under some complex environments. However, in real-world environments, there are many challenging problems such as viewpoint changes and illumination changes. The existing deep learning-based place recognition in extracting feature phases and matching process is both time-consuming. Moreover, features extracted from convolution neural network (CNN) are floating-point type with high dimension. In this paper, we propose deep supervised hashing for place recognition, where we design a similar hierarchy loss function to learn a model. The model can distinguish the similar images more accurately which is well suitable to place recognition. Besides the model can learn high quality hash codes by maximizing the likelihood of triplet labels. Experiments on several benchmark datasets for place recognition show that our approach is robust to viewpoints, illuminations and season changes with high accuracy. Furthermore, the trained model can extract features and match in real time on CPU with less memory consumption.
Impact of Header on Coded Slotted Aloha with Capture

Among the recently proposed non-orthogonal multiple access techniques, the Coded Slotted Aloha (CSA) scheme has been identified as one of the most efficient ones. This scheme relies on the subdivision of a packet into segments and of a slot into slices, in order to enable the transmission of different segments in different slices. With respect to SA, the CSA implementation is more sophisticated, since the header of each segment must contain proper pointers specifying the slices where the other segments of that packet lie. Purpose of this paper is to investigate the impact of these pointers on the CSA throughput by properly considering the capture effect at the receiver. To this aim, a capture analysis accounting for the modulation and the header coding rate in the presence of Rayleigh fading is developed. The numerical results, which also involve a realistic millimeter-wave channel, show that the beneficial interference diversity effect introduced by the segmentation process is not significantly reduced by the overhead due to the pointers, thus confirming the practical usefulness of the CSA scheme.
Human-Robot Visual Interface for 3D Steering of a Flexible, Bioinspired Needle for Neurosurgery

Robotic minimally invasive surgery has been a subject of intense research and development over the last three decades, due to the clinical advantages it holds for patients and doctors alike. Particularly for drug delivery mechanisms, higher precision and the ability to follow complex trajectories in three dimensions (3D), has led to interest in flexible, steerable needles such as the programmable bevel-tip needle (PBN). Steering in 3D, however, holds practical challenges for surgeons, as interfaces are traditionally designed for straight line paths. This work presents a pilot study undertaken to evaluate a novel human-machine visual interface for the steering of a robotic PBN, where both qualitative evaluation of the interface and quantitative evaluation of the performance of the subjects in following a 3D path are measured. A series of needle insertions are performed in phantom tissue (gelatin) by the experiment subjects. User could adequately use the system with little training and low workload, and reach the target point at the end of the path with millimeter range accuracy.
Source Separation With Side Information Based on Gaussian Mixture Models With Application in Art Investigation

In this paper, we propose an algorithm for source separation with side information where one observes the linear superposition of two source signals plus two additional signals that are correlated with the mixed ones. Our algorithm is based on two ingredients: first, we learn a Gaussian mixture model (GMM) for the joint distribution of a source signal and the corresponding correlated side information signal; second, we separate the signals using standard computationally efficient conditional mean estimators. The paper also puts forth new recovery guarantees for this source separation algorithm. In particular, under the assumption that the signals can be perfectly described by a GMM model, we characterize necessary and sufficient conditions for reliable source separation in the asymptotic regime of low-noise as a function of the geometry of the underlying signals and their interaction. It is shown that if the subspaces spanned by the innovation components of the source signals with respect to the side information signals have zero intersection, provided that we observe a certain number of linear measurements from the mixture, then we can reliably separate the sources; otherwise we cannot. Our proposed framework – which provides a new way to incorporate side information to aid the solution of source separation problems where the decoder has access to linear projections of superimposed sources and side information – is also employed in a real-world art investigation application involving the separation of mixtures of X-ray images. The simulation results showcase the superiority of our algorithm against other state-of-the-art algorithms.