Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Restoration of Articulatory Movements: Functional Electrical Stimulation of Orofacial Muscles

Millions of individuals suffer from impairments that significantly disrupt or completely eliminate their ability to speak. An ideal intervention would restore one’s natural ability to physically produce speech. Recent progress has been made in decoding speech-related brain activity to generate synthesized speech. Our vision is to extend these recent advances toward the goal of restoring physical speech production using decoded speech-related brain activity to modulate the electrical stimulation of the orofacial musculature involved in speech. In this pilot study we take a step toward this vision by investigating the feasibility of stimulating orofacial muscles during vocalization in order to alter acoustic production. The results of our study provide necessary foundation for eventual orofacial stimulation controlled directly from decoded speech-related brain activity.
Contactless Respiration and Heartbeat Monitoring of Multiple People Using a 2-D Imaging Radar

Non-contact vital sign monitoring of multiple people can be realized using a radar sensor that is able to provide a high resolution two-dimensional image of the monitored area. This is particularly user-friendly, since no electrodes have to be attached to the body, which is important especially in view of applications like monitoring neonates or burn victims. There are many further applications of remote cardiopulmonary monitoring in the area of home health care, driver monitoring, sleep monitoring, or even monitoring of arrested people in a prison cell. The radar is able to measure the tiny movements of the chest, caused by respiration and heartbeat. Due to the superposition of both movements a sophisticated processing of the measured data is necessary to extract the heartbeat and respiration signal from the measured overall signal. This paper presents radar measurements at 24 and 77 GHz, where vital signs of multiple people have been simultaneously monitored and proposes a signal processing method to separate heartbeat and respiration in the measured data.
The Optimal Setting for Multilayer Modularity Optimization in Multilayer Brain Networks*

Community detection plays a key role in the study of brain networks, as mechanisms of modular integration and segregation are known to characterize the brain functioning. Moreover, brain networks are intrinsically multilayer: they can vary across time, frequency, subjects, conditions, and meaning, according to different definitions of connectivity. Several algorithms for the multilayer community detection were defined to identify communities in time-varying networks. The most used one is based on the optimization of a multilayer formulation of the modularity, in which two parameters linked to the spatial (γ) and temporal (ω) resolution of the uncovered communities can be set. While the effect of different γ-values has been largely explored, which ω-values are most suitable to different purposes and conditions is still an open issue. In this work, we test the algorithm performances under different values of ω by means of ad hoc implemented benchmark graphs that cover a wide range of conditions. Results provide a guide to the choice of the ω-values according to the network features. Finally, we show an application of the algorithm to real functional brain networks estimated from electro-encephalographic (EEG) signals collected at rest with closed and open eyes. The application to real data agrees with the results of the simulation study and confirms the conclusion drawn from it.
Implementation of an Advanced Frequency-Based Hebbian Spike Timing Dependent Plasticity

The brain is provided with an enormous computing capability and exploits neural plasticity to store and elaborate complex information. One of the multiple mechanisms that neural circuits express is the Spike-timing-dependent plasticity (STDP), a form of long-term synaptic plasticity exploiting the time relationship between pre- and post-synaptic action potentials (i.e., neuron spikes). It has been found that in certain cases, for instance at the input stage of the cerebellum, between mossy fibers and granular neurons, the plasticity is not only driven by the timing of the spikes, but also by the oscillation frequency of the inputs. This complex behaviour has been implemented in this work, where we developed a novel form of advanced synaptic plasticity model to be used in a well-established neural network simulator (NEST). The subsequent tests proved the proper functioning of the plasticity and its range of applicability, demonstrating the possibility to adopt it in noisy and variable conditions, similar to the biological settings.
Deep Neural Network for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT Images

The emerging PSMA-targeted radionuclide therapy provides an effective method for the treatment of advanced metastatic prostate cancer. To optimize the therapeutic effect and maximize the theranostic benefit, there is a need to identify and quantify target lesions prior to treatment. However, this is extremely challenging considering that a high number of lesions of heterogeneous size and uptake may distribute in a variety of anatomical context with different backgrounds. This study proposes an end-to-end deep neural network to characterize the prostate cancer lesions on PSMA imaging automatically. A 68Ga-PSMA-11 PET/CT image dataset including 71 patients with metastatic prostate cancer was collected from three medical centres for training and evaluating the proposed network. For proof-of-concept, we focus on the detection of bone and lymph node lesions in the pelvic area suggestive for metastases of prostate cancer. The preliminary test on pelvic area confirms the potential of deep learning methods. Increasing the amount of training data may further enhance the performance of the proposed deep learning method.
Laboratory Samples Allocation Problem

This work aims to solve the optimization problem associated with the allocation of laboratory samples in plates. The processing of each of these plates is costly both in time and money, therefore the main objective is to minimize the number of plates used. The characteristics of the problem are reminiscent of the well-known bin packing problem, an NP-Hard problem that, although it is feasible to model as a linear programming problem, it cannot be solved at a reasonable cost. This work, proposes the implementation of a heuristic algorithm that provides good results at a low computational cost.
On the Use of Non-Contact Capacitive Sensors for the Assessment of Postural Hand Tremor of Individuals with Parkinson’s Disease*

Parkinsonian tremor manifests in different types: rest, postural, and action tremors. The postural tremor occurs while a body part is held straight out from the body in a stable position against gravity. The Unified Parkinson’s Disease Rating Scale (UPDRS), which is a subjective assessment performed by the qualitative judgment of neurologists, is the clinical standard for parkinsonian tremor assessment. Despite the common use of subjective methods, inertial measurement unit (IMU) sensors are largely used in many studies as a motion capture system to objective assessment of tremors. However, this kind of sensor must be attached to the patient’s body, it limits the patient’s movements and requires specific techniques for correct positioning in the limb. In this sense, non-contact capacitive (NCC) sensors are an alternative proposed in this research to record the motor activity of the hand and wrist during a pose against gravity. In order to assess the postural tremor and evaluate this novel sensing technology, data from ten subjects, five with Parkinson’s disease (PD) and five neurologically healthy (H) matched in age and sex, were collected. We analyzed the instantaneous mean frequency (IMNF) of the signals from NCC and gyroscope sensors for both groups. The selected descriptive statistical variables allowed discrimination (p < 0.05) among subjects from H and PD groups while using the gyroscope or the NCC sensor. The obtained results indicate that the NCC sensor can measure the postural hand tremor, and also that frequency features extracted from the collected signals can be used to discriminate subjects from both groups.
A Set Space Model to Capture Structural Information of a Sentence

The context of a sentence is composed of a limited number of words. This leads to the feature sparsity problem whereby the sentence’s meaning is easily influenced by language phenomena such as polysemy, ambiguity and puns. To resolve these problems, the set space model (SSM) uses language characteristics to group features of a sentence into different sets. Afterwards, the proposed feature calculus is used to capture the structural information of the sentence. Experiments have shown that this approach to the relation recognition task is effective. However, at least three weaknesses remain. First, due to the lack of a probabilistic explanation, several aspects of SSM (e.g., filter selection) have not yet been covered. Second, the existing studies have only provided an outline of SSM, and many issues remain unclear. To understand this approach, it is necessary to discuss a suitable example in detail. Third, SSM has been applied only to the task of relation recognition. Case studies of more typical topics (e.g., named entity recognition) will help illustrate the use of SSM’s methodology to manipulate features. This paper develops SSM to cover these problems. It describes a systematic and novel approach to manipulating features of a sentence. In the experimental part, two typical information extraction tasks are performed to demonstrate SSM’s capabilities. Two case studies are considered, and favorable improvements are observed. All of the obtained results surpass those of compared approaches. The experiments also show the influence of sentence structural information on information extraction.
Design and Performance Enhancement of Vivaldi Antenna

In this paper, a computer aided design of Vivaldi Antenna is developed which is used to study the effect of different parameters such as rate of opening of exponential slot and size of the radius of circular slot of the Vivaldi Antenna. The proposed design resulted in enhancement of gain and reduction in reflection losses. COMSOL Multi physics simulator is used to design the proposed Vivaldi antenna.
Magnetic Micromotors for Resilient and Reversible Cargo Transport in and between Microfluidic Environments

In this work we present magnetically propelled micromotors that can capture, transport, and release microscopic cargo in a resilient and reversible manner. In a proof-of-concept experiment, we demonstrate the transfer of a cargo-loaded micromotor between separate microfluidic environments by pipetting for potential chip-to-chip or chip-to-body transfer. This step is important for many biomedical applications of motile microdevices where not everything from preparation to application can happen in a single on-chip environment. A simple propulsion and cargo capture mechanism based solely on rotation allows the presented micromotors to deliver microparticles in high-viscosity medium and confined microchannels without any external trigger except for a rotating magnetic field, employing biocompatible materials only, toward biomedical applications.
Sedimentary record comparison of the Piwniczna and Poprad sandstones (Magura Unit, Outer Carpathians) – a study from the border area of eastern Slovakia and Poland

Thick deep-water sandstone successions are known from many localities in the Outer Western Carpathians. The present study is focused on comparison of deposits of the Piwniczna (PwSM) and Poprad (PpSM) Sandstone members belonging to the Magura Formation of the Magura Unit, outcropping in the eastern Slovakia and Poland border area. At the general level, a close similarity of the sandstone successions was confirmed by this study. However, in more detail, facies analysis shows differences in the thickness of the sandstone successions, in grain size and in the character of intervals between the thick sandstones. Thin section study, though, showed almost the same modal composition in the sandstones analysed. The sandstone successions studied are interpreted as sandy debrites, the PwSM showing transitional facies to turbidites. These deposits represent channelized depositional lobes in the proximal part of a submarine fan with the PpSM closer to the source. The sediment source area was common to both lithostratigraphic units, and was most likely the South-Magura Ridge.
Design of the Clutched Variable Parallel Elastic Actuator (CVPEA) for Lower Limb Exoskeletons

The paper presents a novel clutched variable parallel elastic actuator (CVPEA) mainly consisting of a motor and a flat spiral spring in parallel to actuate the hip of lower limb exoskeletons in sagittal plane. To minimize the power and torque requirement for motor, we put forward three optimization methods including minimizing the rms motor power, the peak motor torque and the peak motor power to determine the optimal PE (parallel elastic) for CVPEA. It is proved that the mechanical energy reduction in the late stance period can be stored in the PE and can be later recycled in the early swing period in a gait cycle according to simulation results. Simulation results also show that using the optimal PE with stiffness of 0.037 Nm/rad, balance position of 0.452 rad determined by minimizing peak motor torque, the rms and peak motor torque, the rms and peak motor can be approximately reduced by 52.3%, 52.9%, 57.5% and 70.2%, respectively, when a subject wearing the CVPEA driven exoskeleton with body weight of 90kg walks at a speed of 0.8 m/s. With this optimal PE, the motor torque and power requirement are largest reduced.
SDN Soft Computing Application for Detecting Heavy Hitters

To avoid distributed denial-of-service (DDoS) attacks or real-time transmission control protocol (TCP) incast in the software-defined networking (SDN) environment, the HashPipe algorithm was developed following the space-saving approach. Unfortunately, HashPipe implemented in the behavioral model (bmv2) cannot be directly executed at a real programming protocol-independent packet processor (P4) switch due to P4 pipeline limitation. Based on the Banzai machine model, this paper shows how to smartly utilize the Banzai atoms to develop HashPipe as a soft computing application in a real P4 switch. Then we propose an enhanced HashPipe algorithm that significantly improves the accuracy of the original HashPipe. The proposed heavy hitter detection is executed at the line-rate of the Tofino P4 switch with the highest process rate in the world.
Pun Detection using Soft Computing Techniques

Pun is a literary device based on wordplay and ambiguity. It is widely used in many varieties of contemporary and literary texts. The popularity of puns as a feature of humor and Figure of speech makes it imperative for computers to be able to understand and process them. With the advent of social media, the use of puns in funny tweets, posts, blogs, and comments has seen a meteoric rise. In this paper, we intend to explore the task of detecting the presence of pun-based humor in sentences. Soft Computing techniques have shown a great affinity for detection and classification tasks. Hence, we analyze the performance of seven supervised classification algorithms for the task of pun detection on a dataset containing 5000 sample sentences collected from internet sources such as social media websites like Twitter and Facebook. Metrics of accuracy, precision, and recall have been used to evaluate the performance of the classifier.
Estimation of Motor Unit Global Firing Rate by Maximum Power Amplitude

Motor unit (MU) global firing rate is widely applied in physiological and clinical investigation. Currently it still remains difficult to measure the MU global firing rate from sEMG. In this study, we propose a new feature of maximum power ampiltude (MPA) from sEMG power spectrum. Based on an analysis of mathematical model and simulated signals, MPA was demonstrated to be highly correlated with the MU global firing rate. The performance of MPA was comparable with features based on sEMG amplitude in the time domain. Moreover, the simulation results showed that the square of MPA changed accordingly with the output force, indicating potential application estimating force using MPA2.
Improving Automatic Tremor and Movement Motor Disorder Severity Assessment for Parkinson’s Disease with Deep Joint Training

Parkinson’s disease (PD) is one of the most severe and common disease globally. PD induces motor system impairment causing symptoms such as shaking, rigidity, slowness of movement, body tremor and difficulty with walking. Clinically, accurately and objectively assessing the severity of PD symptoms is critical in controlling appropriate dosage of Levodopa to prevent unwanted side effect of switching between Dyskinesia and PD. The unified Parkinson’s disease rating scale published by the Movement Disorder Society (MDS-UPDRS) is an validated instrument regularly administrated by trained physician to assess the severity of a PD patient’s motor disorder. In this work, we aim at advancing vision-based automatic motor disorder assessment, specifically hand tremor and movement, for PD patients during UPDRS. Our proposed method leverages information across the two behavior tasks simultaneously via deep joint training to improve each single task’s, i.e., tremor and movement, severity classification rate. We evaluate our framework on a large cohort of 106 PD patients, and with our proposed deep joint training framework, we achieve accuracy of 78.01% and 80.60% in right and left hand movement binary classification; in terms of tremor severity classification, our approach obtains an enhanced recognition rates of 72.20% and 71.10% for right and left hand respectively.
Simulation of the efficiency of inner hair cell secretion in the auditory pathway

Sound coding involves several stages of processing along the auditory path. Specifically, the Inner Hair Cells (IHC) act as sensory receptors and transduce acoustic information -frequency, intensity and duration of the stimulus- into neuronal signals. In this work, a stochastic model was implemented to achieve a better understanding of the IHC-auditory nerve synapse, specifically, the process of Ready Releasable Pool (RRP) vesicle exocytosis, a complicated process to study experimentally because current protocols do not provide adequate temporal resolution, in the order of milliseconds. The presented model allows predicting the efficiency of glutamate release towards explaining maturation changes or disease impacts in the auditory pathway.
Nonlinear Fuzzy State-Space Modeling and LMI Fuzzy Control of Overhead Cranes

The development of feedback control systems for overhead cranes is of great importance due to many potential applications and advantages over manual operation concerning stability and robustness. We represent the key nonlinear dynamics of cranes in a compact state-space fuzzy model. The fuzzy model assists the design of a fuzzy controller through parallel distributed compensation. A conservative linear-matrix-inequality feasibility problem is formulated so that a solution guarantees closed-loop Lyapunov stability, constrained inputs, quick positioning of the supporting cart and suppression of load oscillations. Due to the nonlinear nature of the fuzzy model and controller, Jacobian linearization at a hyperbolic equilibrium is avoided. The proposed fuzzy controller for cranes has shown to be effective, robust and able to move loads smoothly even after collisions. Constrained and smooth inputs avoid actuator saturation and tend to increase its lifetime.
Determination of a tactile feedback strategy for use in robotized percutaneous procedures

Remote manipulation in robotized percutaneous procedures can offer increased safety to radiologists as well as patients. Providing feedback to the radiologist on needle-tissue interactions is however mandatory in addition to the medical images. A tactile feedback strategy is developed in this paper. Two types of information are considered: tissue puncture and nature of tissues. A haptic device is developed for that purpose, using a tactile display to provide information. Adequate signals are identified experimentally, with analysis of reaction times and the ability to discriminate one information from the other.
A Traffic Sign Image Recognition and Classification Approach Based on Convolutional Neural Network

In this paper, the traffic sign recognition method based on CNN is studied by using image processing and machine vision processing technology combined with the application of in-depth learning in target classification. A traffic sign recognition method with high recognition efficiency and high efficiency is proposed. The scheme uses the method of training cascade classifiers based on HOG features to detect traffic signs. At the same time, to ensure that all traffic signs are detected, an improved CNN model is trained to determine whether there are traffic signs in the candidate area, and the final decision is made as the last level of the cascade classifier. The simulation results show that the scheme can accurately locate the location of traffic signs, greatly reduce the false detection rate, improve the accuracy rate, and lay the foundation for subsequent traffic sign recognition.
Continuous Tracking of Changes in Systolic Blood Pressure using BCG and ECG

Blood pressure (BP) is an important physiological marker of human health. It is commonly measured by a cuff-based monitor via either auscultatory or oscillometric methods. Recently, significant research has been conducted to mathematically estimate BP from pulse transit time (PTT) to enable cuffless and continuous BP measurement. In this research, a new time reference, RJ interval, which is the time delay between electrocardiogram (ECG) R peak and ballistocardiogram (BCG) J peak was evaluated to determine if it can be used as a surrogate of PTT in cuffless BP estimation. Biomedical signals from ten healthy subjects were acquired by BCG, ECG and PPG sensors and the continuous reference BP data were collected by a cuff-based Finometer PRO BP monitor. An exponential model was employed to estimate systolic blood pressure (SBP) using RJ interval and PTT. RJ intervals extracted from ECG and BCG were shown to be useful in evaluating trends of SBP and can be the surrogate of PTT in cuffless SBP estimation.
Delta Parallel Robotic Manipulator Tracking Control using Fractional Order Controllers

Tracking control of robotic manipulators presents many challenges on the controller design due to the system nonlinear behavior. This paper presents the design of a tracking control for a parallel robotic manipulator using a fractional order PID controller with the feedback linearization technique. The kinematic and dynamic models of the manipulator are obtained, and a parametric identification of the dynamic model is performed using the recursive least squares algorithms. A MSC-ADAMS/MATLAB co-simulation model of the manipulator is built to perform the identification and control tasks. The inverse dynamics method gives a linearized and uncoupled model of the robotic system, which is employed to design the fractional order PID controller. The fractional order PID controller is contrasted with an integer order PID controller on performing tracking tasks. A quantitative performance analysis of the controllers is made calculating the performance indices for different tracking tasks. Obtained results show that the fractional order PID controller has a better performance on tracking tasks than the integer order PID controller.