Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive ICA for Speech EEG Artifact Removal

Recently, in cognitive neuroscience, artificial stimuli such as single sentences have been replaced by more naturalistic stimuli such as continuous speech. Since it is already known ‘where’ in the brain language is processed, the next crucial step is to investigate ‘how’ these neuronal circuits and processes work. Thus, it is necessary to apply experimental procedures with a high temporal resolution such as electroencephalography (EEG) in order to capture and identify these mechanisms. However, EEG is highly prone to measurement errors as the surface electrodes collect all kinds of electromagnetic noise from physiological and non-physiological sources. Here, we present a procedure to remove those artifacts (with a special focus on eye artifacts) and provide evidence that it is possible to extract event related potentials (ERPs) from EEG data recorded during listening of an audio book. We developed an evaluation pipeline, tested on EEG-data of 36 participants. The pipeline consists of two major steps: spectral filtering (bandpass: 1 Hz-20 Hz) and a custom version of independent component analysis (ICA) filtering. Thus, we defined one channel (Fp1) as our electro-oculogram channel (EOG) and tested which independent components significantly correlate with this channel. All independent components that had a correlation above a fixed threshold were removed. This procedure is highly reproducible and allows to extract clean ERPs from EEG data during continuous speech perception. We show that the ERP responses to adjectives are different from ERPs to verbs in shape as well as latency. We suggest that this advancement in evaluating EEG data may further improve neurolinguistics research and is a further step to develop a unified evaluation pipeline for these kinds of data.
Novel Scalable DNN-based Relay Selection Scheme for Wireless Powered Communication Networks

In this paper, we propose an optimal relay selection scheme based on a deep neural network (DNN) to evaluate and minimize the outage probability (OP) in cooperative wireless powered communication networks (WPCNs). We explicitly split the relay selection problem into two parts: OP estimation and relay selection itself. We first consider the task from the perspective of the supervised learning regression problem. We train offline the DNN-based model on synthetic data. The proposed model is easy to scale for a network of any size since it predicts OP for each relay separately. Then, based on the results obtained in the previous step, the node with a minimum OP value is selected. Simulation results show that the proposed DNN-based relay selection scheme achieves minimum OP and exhibits the lowest mean square error than the models based on the state-of-the-art machine learning approaches.
Exploring the synergies of biomass-based top-cycle CHP plants with hydrogen for industrial applications

Combined heat and power plants (CHP) maximise the usable energy from combustion processes. By providing both usable heat and electricity, it is a flexibly adjustable technology to industrial plants’ energy demand. Today, CHP units are mostly fueled by fossil energy carriers, such as natural gas. However, CHPs must be operated with renewable fuels to comply with net-zero transition pathways. This study explores the economics of a biomass CHP plant in combination with an electrolyser, a dedicated renewable generation plant, and a small hydrogen storage tank for a Spanish case study. Using an optimisation model that combines electrolyser and biomass CHP operations, the cost-optimal operation is explored, considering various electricity prices and other scenarios. Under the assumption that heat generation from alternative sources can be flexibly reduced, we investigate if there are synergies between hydrogen and biomass use for CHP plants.
Symbolic Regression for Fault Prognosis and Remaining Useful Life Estimation*

We present a hybrid scheme for prognostics and system health management, which combines system modeling methods and regression-based approaches. Along these lines, we perform parameter trending using symbolic regression, by implementing a genetic programming algorithm that integrates the system model based on the available sensor data. The obtained fault function is an analytical expression for the progression of the system fault in time, which provides valuable insights on its causality. For comparison purposes, we also employ a dynamic degradation regression model that encompasses as health indicators inferential sensors that have been optimized by combining symbolic regression and information theory. To highlight the effectiveness of the proposed framework, both of the aforementioned approaches are applied to a dynamic model of a cross-flow plate-fin heat exchanger toward predicting fault occurrences and estimating the remaining useful life of the system, for various levels of measurement noise.
Research on autonomous control technology of group formation imitating the behavior of geese flock

To solve the problem of group formation control, a leader-wingman formation control law imitating the behavior of geese flocks is designed, and a formation change strategy based on auction mechanism is proposed, which shortens the time and distance of formation change. Aiming at the problem of formation control in the turning section, a wingman tracking algorithm based on the advanced tracking point is given. The flight test shows that the algorithm can effectively solve the problem of long trajectory and tail swing of wingman in turn section.
Prediction of Malaria Plasmodium Stage and Type Through Object Detection

The use of image processing, artificial intelligence in general, and objects detection in particular for the diagnosis of malaria is increasingly remarkable. In the present work, we suggested a comparison of two objects detection models which is not only capable of detecting the infected blood smears cells but also it enables to distinguish the plasmodium different species and the parasitic stage of malaria. To create our two models, transfer learning from the Faster-RCNN and YOLO models have been used with the MP-IDB database (Malaria Parasite Image Database for Image Processing and Analysis). Then, we have followed three main steps to develop our approach of objects detection for malaria: the creation of annotated databases, image preprocessing, and fine-tuning the two pre-trained models on our annotated database. The Faster R-CNN-based model produced better results than the Yolo-based one, with a mAP @. 50IOU of 0.76 versus 0.3.
3D Brain MRI Segmentation using Deep Neural Network

Magnetic Resonance Images (MRI) have been utilized by radiation oncologists to identify the size of tumours within the brain.The accurate identification of brain tumours from 3D MRI images is essential for proper diagnosis. Our proposed model introduces a network capable of segmenting 3D images using sparsely labeled data. This network is an enhanced version of the u-net architecture with attention network and utilizes 3D operations, without the need for a pre-trained network. The effectiveness of this approach was evaluated on the well-known BraTS 2018 brain dataset and achieved a Dice Similarity Coefficient score of (0.95, 0.89, 0.95) for the complete, core, and enhancing regions, respectively
Large-Scale Radar Localization using Online Public Maps

In this paper, we propose using online public maps, e.g., OpenStreetMap (OSM), for large-scale radar-based localization without needing a prior sensing map. This can potentially extend the localization system to anywhere worldwide without building, saving, or maintaining a sensing map, as long as an online public map covers the operating area. Existing methods using OSM only use route network or semantics information. These two sources of information are not combined in the previous works, while our proposed system fuses them to improve localization accuracy. Our experiments, on three open datasets collected from three different continents, show that the proposed system outperforms the state-of-the-art localization methods, reducing up to 50% of position errors. We release an open-source implementation for the community.
A lightweight high-voltage boost circuit for soft-actuated micro-aerial-robots

Flight is an energetically expensive task. While aerial insects can effortlessly fly through natural environments, achieving power autonomous flights in insect-scale robots remains a major challenge. In prior works, we developed soft-actuated insect-scale aerial robots that demonstrated unique capabilities such as in-flight collision recovery and somersaults. However, the soft dielectric elastomer actuators (DEAs) have low efficiency (< 20%) and require a high driving voltage (>600 V). These properties represent formidable obstacles for soft aerial robots to achieve power autonomous flights. In this work, we developed a 127 mg boost circuit that can convert a 7.7 V DC input into a 600 V and 400 Hz output for driving a 120 mg DEA. It has an equivalent capacitance and resistance of 20 nF and 5 $\mathbf{k}\Omega$, respectively. The DEA is assembled into a 158 mg aerial robot, which can demonstrate liftoff while carrying the boost circuit as a payload. Although the robot remains tethered to an off-board power supply, this result represents a first step towards achieving power autonomy in soft aerial robots.
AI Framework for Fetal Health Risk Prediction

Cardiotocography is an important technique used in the field of obstetrics to monitor the fetal heart rate and the mother’s uterine contractions throughout a pregnancy. Analysis of cardiotocographs allows for thorough understanding of fetal health, and thus, can improve fetal and maternal health outcomes. Traditional methods of monitoring fetal movement and uterine contractions through human analysis of cardiotocographs is not always reliable since this analysis is often subjective. Using Artificial Intelligence to analyze this data would, for these reasons, allow for more accurate predictions about the health of the fetus. Data consisting of 2126 cardiotocograph recordings of pregnant women has been taken from the University of California, Irvine and the University of Porto to conduct a study in monitoring fetal health using Artificial Intelligence. This study uses both IBM Watson and Python-based AI models to predict fetal health. The primary outcome of this study is a model that is trained to predict fetal health outcomes. The model provides 3 predictions: the number 1 corresponds to a predicted healthy fetus; a prediction of 2 corresponds to a fetus that suspectedly has a health problem, but it is not known for sure; the number 3 corresponds to a definitive health problem for the fetus. This study has found that the CatBoostClassifier coded in Python has a 95.3% accuracy, the highest of all models used.
Control Constrained Game Theoretic Differential Dynamic Programming

In this work, a control constrained version of the continuous time game theoretic differential dynamic programming (GT-DDP) algorithm is presented. The convergence of the GT-DDP algorithm is analyzed, and it is shown that control constraints can be successfully applied through solving quadratic programs during the control update phase of the GT-DDP. The control constrained GT-DDP is applied to the trajectory tracking problem of a quadrotor. Additionally, the proposed algorithm is demonstrated on a real world experiment of a pursuit-evasion game between two quadrotors to show its ability in planning trajectories for multiple quadrotors under different control constraints.
Unveiling the Temporal and Spectral Relationships between Seismocardiogram Signals, Systolic Time Intervals and Thorax Characteristics

Investigating the systolic time intervals (STIs) can provide information regarding cardiovascular health and autonomic nervous system. Among these STIs, the pre-ejection period (PEP) and left ventricular ejection time (LVET) have been found to be closely related to the ventricular failure and many other indicators. Recently, it has been shown that the seismocardiogram (SCG) signal can be used to derive hemodynamic parameters; thus, can be leveraged in the design of cardiovascular monitoring systems. Accordingly, in this work, (i) SCG-based PEP and LVET regression models leveraging both temporal and spectral analysis have been developed, (ii) the most distinctive features in STI estimation have been determined, and (iii) the relationship between the thorax impedance values and SCG-derived features has been investigated to quantity inter-subject variability. Overall, PEP and LVET could be estimated with low RMSE (7.436 and 11.022 ms, respectively) using extreme gradient boosting trees. Moreover, it was deduced that the thorax impedance characteristics indeed have high correlation with the SCG features, specifically in the 40 - 100 Hz range. Overall, these findings can potentially allow more useful and informed SCG-based STI analysis by providing valuable insights regarding SCG and thorax characteristics.
Grant Free NOMA with Deep Learning for Massive IoT Applications

Internet of Things (IoT) has become a very hot research topic in recent years because it aims to ensure one of the major goals of 5G and beyond– connecting any physical device without human interaction. This type of data has been defined as Machine-Type Communication (MTC). A major design issue is how to sustain the network performance when a very large number of devices access the network at the same time. To resolve this issue, many studies propose new spectrum efficient methods that aim to increase the throughput, energy efficiency and other network performance parameters. One of these solutions is NonOrthogonal Multiple Access (NOMA), an alternative to wellknown orthogonal multiple access schemes. Even though NOMA was initially suggested for coordinated networks, it may be a feasible solution for the Massive IoT networks without coordinated access. The next generation networks should decide in a smart way. 6G-IoT networks also should be more intelligent with self-coordination. In this study Deep-Q-Network (DQN) based NOMA is proposed for the uncoordinated uplink transmission in IoT networks. According to the results proposed method is outperform the NOMA scheme with random selection in terms of throughput and power consumption.
Existence of a Unique Invariant Measure and Ergodic Property in AIMD-based Multi-resource Allocation

Distributed resource allocation arises in many application domains, such as smart energy systems, intelligent transportation systems, cloud computing, edge computing, etcetera. To realize many of these applications, agents in a network may require multiple shared resources to complete a task and aim to maximize the network utility. Additionally, they may demand resources based on their preferences. Furthermore, they may not wish to share their cost functions, partial derivatives of the cost functions, etc., with other agents or a central server; however, they share their resource demands with the central server that aggregates the demands and sends one-bit resource-capacity constraint notification in the network. The single-resource allocation algorithms are inefficient and provide sub-optimal solutions for multi-resource allocations, especially when the cost functions are multi-variate and non-separable. We present additive increase and multiplicative decrease algorithm (AIMD)-based distributed solutions for multi-resource allocation. We formulate the resource allocations problem over finite window sizes and model the system as a homogeneous Markov chain with place-dependent probabilities. We show that the time-averaged allocations over the finite window size converge to a unique invariant measure. We also show that the ergodic property holds for the model.
Adaptive Heading for Perception-Aware Trajectory Following

This paper presents an adaptive heading approach for perception awareness during trajectory following. By adapting the heading of a robot to improve the feature tracking in the current mapped environment, the accuracy in localisation can be improved. This can have a significant advantage for autonomous operations in GPS-denied environments such as subsea or in caves. The aim of the proposed approach is to position the sensor used for perception and feature tracking in such a way that it; obtains a view that contains a good observation of the previously mapped environment, face forward along the direction of travel, reduces the change in heading and view the perceived environment along the surface's estimated normals. These 4 objectives create a weighted utility function that is used to find the most beneficial heading. The benefit is a system that improves feature tracking for simultaneous localisation and mapping (SLAM) while considering the safety of the robot by being aware of its surrounding. To sense the environment, a simulated sensor is discretised to a set of vertical rays based on the vertical field of view. The vertical rays are swept 360 degrees around a position to evaluate for a new heading. This allows for the simulated sensor data from ray casting to be reused and therefore reduces the computational load to find the heading which maximises the utility function. The paper is focused on holonomic robots capable of controlling the robot's heading or sensor orientation independently from the position. We present results and evaluation in a simulated environment where we show a great improvement in the SLAM's pose estimation. In addition, we endow an autonomous underwater vehicle (AUV) with the proposed approach during field trials and present the result in two different environments.
Multi-Head Attention Machine Learning for Fault Classification in Mixed Autonomous and Human-Driven Vehicle Platoons

Connected Autonomous Vehicle (CAV) platoons have been extensively studied to protect against cyber and physical vulnerabilities. Faults can occur in all layers of the platoon system or could be introduced by impaired human drivers. Since different types of faults may require different fault resolution methods, identifying the fault class facilitates the selection of the best mitigation strategy. This paper introduces a Multi-Head Attention Machine Learning (MHA-ML) approach to classify a set of five different faults and abnormalities in mixed autonomous and human-driven vehicle platoons. Autonomous vehicles can face actuator faults, False Data Injection (FDI) attacks, and Denial-of-Service (DoS) attacks, while abnormalities such as drunk or distracted human drivers could occur. MHA-ML is developed to identify faulty vehicle behavior over long sequences of sensor measurements. MHA-ML is trained on a mixed platoon simulation model and then tested on mobile laboratory robots. The experiment classifies the five fault categories with 90% accuracy and outperforms a baseline recurrent neural network approach.
Characterisation of Antagonistically Actuated, Stiffness-Controllable Joint-Link Units for Cobots

Soft robotic structures may play a major role in the 4th industrial revolution. Researchers have successfully demonstrated the advantages of soft robotics over traditional robots made of rigid links and joints in many application areas. Variable stiffness links (VSL) and joints (VSJ) have been investigated to achieve on-demand forces and, at the same time, be inherently safe in interactions with humans. However, a thorough characterisation of soft and rigid robotic components is still required. This paper investigates the influence of antagonistically actuated, stiffness-controllable joint-link units (JLUs) on the performance of collaborative robots (i.e. stiffness, load capacity, repetitive precision) and characterizes the difference compared with rigid units. A JLU is made of a combination of a VSL, a VSJ, and their rigid counterparts. Experimental results show that the VSL has minor differences in terms of stiffness (0.62 ∼ 0.95), output force (0.93 ∼ 0.94), and repetitive precision compared with the rigid link. For the VSJ, our results show a significant gap compared with the servo motor with regards to maximum stiffness (0.14 ∼ 0.21) and repetitive position precision (0.07 ∼ 0.25). However, similar performance on repetitive force precision and better performance on the maximum output force (1.54 ∼ 1.55 times) are demonstrated.
Data-driven Model Predictive Control for Drop Foot Correction

Functional Electrical Stimulation (FES) is an effective method to restore the normal range of ankle motion in people with Drop Foot. This paper aims to develop a real-time, data-driven Model Predictive Control (MPC) scheme of FES for drop foot correction (DFC). We utilize a Koopman operator-based framework for system identification required for setting up the MPC scheme. Using the Koopman operator we can fully capture the nonlinear dynamics through an infinite dimensional linear operator describing the evolution of functions of state space. We use inertial measurement units (IMUs) for collecting the foot pitch and roll rate state information to build an approximate linear predictor for FES actuated ankle motion. In doing so, we also account for the implicit muscle actuation dynamics which are dependent on the activation and fatigue levels of the Tibialis Anterior (TA) muscle contribution during ankle motion, and hence, develop a relationship between FES input parameters and ankle motion, tailored to an individual user. The approximation, although computationally expensive, leads to reformulating the optimization problem as a quadratic program for the MPC problem. Further, we show the closed-loop system’s recursive feasibility and asymptotic stability analysis. Simulation and experimental results from a subject with Multiple Sclerosis show the effectiveness of the data-driven MPC scheme of FES for DFC.
Virtual Bonding in Ethernet Transmission Wireless Backhauled Links

In order to achieve higher throughput and robust redundancy, bonding ports has been a widely-used technique in wired networking since a long time. With the evolution of 5G, deployment of wireless backhaul links are on the rise, since they require less infrastructure. A single wireless link throughput is limited due to its hardware capabilities, therefore bonded links can be a suitable alternative to provide higher throughput, while keeping same infrastructure. Radios in wireless backhaul links may not be present in the same premises, which demands smart techniques to bond the wireless backhaul links. This paper presents a novel approach of virtually bonding different wireless backhaul links to provide a single control plane for the north and south bound devices for control plane, Management and Data Plane Protocols. Additionally, this approach will scale the extension of Layer 2 and Layer 3 domains of a network, along with a reduction in the manual configuration. Our simulation results show that our approach increased Transmission Control Protocol (TCP) throughput by 59.5% and 61.29% in one and two links scenarios respectively.