Miracle-dz/UniFAR-FTUs huggingface dataset:
Modal‐based fluid structure coupling in room acoustics considering non‐deterministic properties

Nowadays fluid structure interaction methods gain more and more importance. For room acoustical problems the coupling to realistic boundary conditions, absorbers and reflectors, is still a remaining challenge in the optimization process for the frequency response of the acoustic volume. The following work will present a coupling method based on the Component Mode Synthesis and Hamilton's Principle for the steady state response of linear room acoustic problems. Therein realistic boundaries like absorbers are coupled with the help of their related frequency dependent impedances. For a more physical interpretation uncertain parameters are considered within the input data. Basic possibilistic and probabilistic concepts are applied on benchmark configurations to estimate the variation in frequency response functions from uncertain input data. (© 2010 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Direction of Arrival (DoA) Estimation Performance in a Multipath Environment with Envelope Fading and Spatial Correlation

The Direction of Arrival (DoA) estimation method is widely researched and applied to a diverse array of structures under complex environments. However, the performance investigation of the DoA estimation method for 5G millimeter-wave (mmWave) based Vehicle-to-Everything (V2X) applications operating in a multipath fading environment with channel-phase random variable and spatial correlation is limited to the best of our knowledge. Therefore this work investigates the scenario mentioned above in a single receiver system equipped with the multiple-antennas. The performance of the proposed scheme is evaluated through extensive Monte Carlo Simulations in MATLAB. The effectiveness of the simulation is validated against the performance metric Cramer-Rao Bound (CRB) for the AWGN case (i.e., thermal noise). Simulation results assisted in visualizing a real trend and showed that the root mean square error (RMSE) statistics are improved for spatial correlation consideration in the multipath fading environment.
Robust & Compact End-to-End Hindi Language ASR System

Deep neural network architectures are highly in-corporated in modern (progressive) automatic speech recognition(ASR) systems which outperforms conventional and hybrid systems. Even after adapting to such system we still facing a problem to make balance between accuracy and model size (no. of trainable parameters). We present our experiment in building a robust encoder-decoder based end-to-end ASR model for the Hindi language. Starting with, we have trained the QuartzNet model with two different architectures QuartzNet-5x5 and QuartzNet-15x5 by keeping the same training parameters and dataset. The speech corpus consists of 2648.9 hours of Hindi labelled audio data collected from various domains and sources. We have analyzed the performance of both models and observed that QuartzNet-15x5 has a radical improvement of 24% in accuracy. For building and training the models, we have used the PARAM SIDDHI AI system and training recipes from the open source NeMo toolkit.
Improved Partial Discharge Signals Analysis Using Discrete Wavelet Transform

It is well known that during the operating life of high voltage (HV) equipments, the insulating materials of which they consist are subjected to structural changes, which can lead to the degradation of insulation properties. Early detection of these changes in insulation allows by adequate maintenance the extension of the life of HV equipment. By partial discharge (PD) detection, it is possible to determine how damaging these changes are to the insulation of HV equipment. Thus, this article presents the phenomena and steps by which measurements for PD detection can be performed. It also presents the analysis and filtering using Discrete Wavelet Transform (DWT) features, which are superior to conventional filtering methods, for signals describing the main characteristics occurring in PD. In addition, experimental tests performed on some of equipments and measurement schemes suitable for PD determination and analysis are also presented.
Performance Analysis of Spiking Neural Networks with Memristive Synapse in Detecting and Classifying RF Wave Signals

Nowadays, the Spiking Neural Networks (SNNs) have been gaining interest because of their energy efficiency and importance in neuromorphic computing. SNN is known to exhibit good performance for processing sequential data. In this work, we conduct a simulation study to utilize SNN for classifying and detecting simple Radio Frequency (RF) wave signals, which are sequential time-series data. Our simulator implements the training procedure of SNN based on Spike Timing Dependent Plasticity (STDP) and non-linear weight update representing memristive synapses. The goal of trained SNN is to detect and classify simple RF wave signal generated with Universal Software Radio Peripheral (USRP) under various SNR conditions. Our simulation results demonstrate that SNN successfully detects the target signal in both low SNR and high SNR.
Singularity of Navier-Stokes Equations

Singularity of Navier-Stokes equations is uncovered for the first time which explains the mechanism of transition of a smooth laminar flow to turbulence. It is found that when an inflection point is formed on the velocity profile in pressure driven flows, velocity discontinuity occurs at this point. Meanwhile, pressure pulse is produced at the discontinuity due to conservation of the total mechanical energy. This discontinuity makes the Navier-Stokes equations be singular and causes the flow to become indefinite. The analytical results show that the singularity of the Navier-Stokes equations is the cause of turbulent transition and the inherent mechanism of sustenance of fully developed turbulence. Since the velocity is not differentiable at the singularity, there exist no smooth and physically reasonable solutions of Navier-Stokes equations at high Reynolds number (beyond laminar flow). The negative spike of velocity and the pulse of pressure due to discontinuity have obtained agreement with experiments and simulations in literature qualitatively. AMS subject classifications: 76D03, 76D05, 76E05, 76F06
Designing Gamified Application: An Effective Integration of Augmented Reality to Support Learning

Early childhood learning is an important concern for the concept building of children. Students have been taught with traditional methods from the beginning. However, these methods lack kids’ attention. Application of Augmented Reality (AR) in a traditional way of teaching can help students in better engagement and better learning. In this research, an ABCD-AR mobile based application is designed to overcome the flaws in the traditional education system. While designing AR mobile applications, certain guidelines need to be considered that cause problems for kids in their learning. These may include but are not limited to small screen size and irregularity. Considering these problems, this study proposes 23 usability principles for AR based learning applications for kids. These identified usability principles are validated by human experts. Furthermore, experiments are carried out to evaluate the application in terms of efficiency, effectiveness, learn-ability, user satisfaction & engagement. The results implies that the developed game-based application is highly effective and efficient because the mean of marks obtained by students before using ABCD-AR application is lower (M = 4.97, SD = 1.650) than the marks obtained after using ABCD-AR application (M = 7.97, SD = 1.608). Similarly, results indicate that mean of time taken to perform tests without using ABCD-AR application (M = 7.97, SD = 1.650) is higher as compared to with using it (M = 4.37, SD = 1.608). Majority of the students were entertained by playing the game while learning, and felt motivated to continue based on the game’s scenario due to the variety of activities included. Teachers and parents involved in these experiments have shown satisfaction towards the use of ABCD-AR application in learning process of preschool kids.
Design and Implementation of Network Data Analytics Function in 5G

For network automation empowered by advanced artificial intelligence (AI) and machine learning (ML) technologies, the third generation partnership project (3GPP) introduced a novel network function called network data analytics function (NWDAF), which collects network data and provides valuable analytics results. Based on the up-to-date NWDAF specification, we implement NWDAF including analytic logical function (AnLF) and model training logical function (MTLF). Our experimental study on analytics and ML model provisioning services demonstrates the feasibility of the NWDAF testbed.
Profiling of low back pain patients for the design of a tailored coaching application

Low back pain is a major public health problem that is the leading cause of disability in most countries. The recommendations are to propose personalized physical activity programs according to the individual characteristics (motivation, preferences, attitude towards physical activity) of each patient. However, the current management of low back pain includes rehabilitation programs that are costly and/or not always personalized. The objective of this research is to better understand the characteristics of people with chronic low back pain to identify typical profiles to inform the design of a tailored coaching mobile application. We selected eight concepts and associated scales to characterize the population: general health status (MYMOP), treatment burden (TBQ), quality of life (EQ-5D-5L), level (IPAQ), fears and beliefs (FABQ-AP) and motivation (EMAPS) for physical activity, emotional state (PHQ-9) and feeling of self-efficacy (FC-CPSES). These questionnaires were administered online via an e-cohort of low back pain patients. 193 patients with chronic low back pain were included. A clustering analysis allowed us to distinguish 4 profiles: (1) “Unmotivated,” characterized by the lowest motivation and the highest amotivation toward physical activity (i.e., they do not understand why it is useful), (2) “Cautious” with low motivation and having the highest fears and beliefs toward physical activity, (3) “Depressed” with good intrinsic motivation but also a significant level of depressive symptoms, and (4) “Confident” also showing good intrinsic motivation and having the highest self-efficacy. These four profiles of patients will be used to design a coaching mobile application tailored along the priorities (e.g., physical activity, pain management) and types of motivational messages displayed on the tailored mobile application.
Analysis and Application of Sensor Data Collected from Strawberry Greenhouse

Attempts to contribute to increasing the production of crops by using AI and IoT technologies are increasing. In particular, since strawberry is an environmentally sensitive crop, it is necessary to apply smart agriculture. Therefore, in this paper, to optimize the strawberry cultivation environment, the cultivation environment was analyzed based on sensor data collected from a specific strawberry cultivation farm. We generated a predictive model based on a multivariate time-series data set collected by temperature-humidity, CO2, soil, and light sensors. Also, we applied a deep learning model called StemGNN that considers the correlation between variables by using a graph neural network. In the future, we are going to conduct research such as environmental prediction, abnormality detection, and growth prediction, and plan to increase strawberry cultivation efficiency by accumulating more diverse data and creating optimal scenarios by using multiple models.
Application of LSTM Model for Western Music Composition

Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18–24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon.
Chemically decorated microalgae for environmental application

The presence of polychlorinated biphenyls (PCBs) in different natural matrices and consequent toxic effects represent a public health issue. Due to their bioaccumulation, they have negative effects on different living organisms, including humans. Here we report a preliminary investigation of suitability of a microalgae-based technique for the bioremediation of marine sediment and water column. Phaeodactylum tricornutum, a model benthic diatom is tested for removal of different biphenyl molecules from sea water solutions. Moreover, cells are individually modified with a polydopamine coating, to enhance both their resistance in harsh environmental conditions and the capability of remediation to hydrophobic pollutants.
Embedding properties of sets with finite box-counting dimension

In this paper we study the regularity of embeddings of finite-dimensional subsets of Banach spaces into Euclidean spaces. In 1999, Hunt and Kaloshin (1999 Nonlinearity 12 1263–75) introduced the thickness exponent and proved an embedding theorem for subsets of Hilbert spaces with finite box-counting dimension. In 2009, Robinson (2009 Nonlinearity 22 711–28) defined the dual thickness and extended the result to subsets of Banach spaces. Here we prove a similar result for subsets of Banach spaces, using the thickness rather than the dual thickness. We also study the relation between the box-counting dimension and these two thickness exponents for some particular subsets of .
Intrusion Detection System for Bluetooth Mesh Networks using Machine Learning

Bluetooth Low Energy (BLE) mesh network is gaining popularity in the IoT sector due to its simple network configuration, network extension and security features. Device security is a concern for IoT systems and is crucial in detecting device attacks. This paper proposes a machine learning based intrusion detection system (IDS) targeted for the BLE mesh networks. The setup consists of nine ESP32 BLE mesh devices and one raspberry pi. The IDS detects known threats, such as black hole attacks and grey hole attacks, in the BLE mesh network using a single watchdog node. The watchdog setup continuously sends network layer data to the Message Queuing Telemetry Transport (MQTT) broker. And the database stores the BLE mesh packets with the timestamp. The target attack node uses a push button to switch between attacks for evaluating IDS. The system uses machine learning algorithms to detect network attacks. The project analyses the performance of attack detection when the location of the watchdog is changed. The machine learning model classifies legitimate, grey hole attack and black hole attack. Finally, a web application shows the BLE mesh network detection status.
Vision Based Deformable Wires Recognition using Point Cloud in Wire Harness Supply

Wire harness assembly is one of the essential tasks in the automation industry. However, it is still being completed by human work owing to several difficulties. In particular, the wire, which is the target object, is a deformable object, and is significantly thin. The features of the wire require other approaches that are suitable for working conditions in terms of recognition as well as robot manipulation. The dual-arm robot must preemptively hold the wire before the wire harness assembly. In this study, we presented a methodology for recognizing cables using point clouds obtained from RGB-D sensors on wire harness supply designed to perform this task. In the proposed three stages, we presented a method for distinguishing the sub-cables and connectors of the wires by applying several clustering methods and color similarity comparisons, and calculating the gripping position of the robot. In addition, we verified our method through experiments on several cases.
Tethya meloni (Porifera, Demospongiae): a promising bioremediator species and source of marine natural products

One of the main problems of aquaculture is the environmental impact of its effluents, rich in particulates and dissolved nutrients from feces and undigested feed, which can promote bacterial growth, including pathogenic bacteria. This process is particularly harmful in the case of terrestrial aquaculture, where contaminants can accumulate in a confined area. In this case, aquaculture discharges threaten the health of the habitat. This paper shows studies aimed at evaluating the aptitude of the demosponge Tethya meloni to be proposed as a new bioremediator species for the treatment of effluents from land-based fish farm. Our results demonstrated that T. meloni can effectively control the growth of Vibrio parahaemolyticus even at high concentrations (106 CFU mL-1). Furthermore, preliminary results on characterization of natural products obtained from T. meloni denote a potential commercial value that make this species suitable as bioremediator even more attractive for mariculture companies.
Traffic Density Dependent Fine Tuning of Green Signal Timing for Faster Commute

With the ever-increasing road traffic density, controlling the traffic becomes an enormous task. Since the number of people in the traffic police department is limited, it becomes difficult to manage the traffic in all city areas manually. The current traffic control signal timing is fixed and does not vary depending on the traffic density. The proposed Real-Time Auto-mated Traffic Signal (RTATS) system detects, tracks, and counts the vehicles on the road using You Only Look Once (YOLO), Euclidean-distance tracker, Line of Interest (LOI) algorithms, and Computer Vision techniques to calculate to fine-tune the Green Signal Time (GST). On the basis of the video (CCTV) surveillance camera data, the traffic flow, density, and average speed of vehicles can be calculated. The proposed RTATS system develops an algorithm for dynamically adjusting the green signal time (DGST). It varies the green signal timing based on the road traffic density at the junction and the average traffic volume contained by each type of vehicle on the road. The system will enable the fine-tuning of Green Signal Time (GST) and maximize the traffic flow with minimum human intervention.
Model-Based Reinforcement Learning for Wireless Channel Access

In this paper, we study a wireless channel access method using model-based reinforcement learning in limited spectral resources. The proposed method maximizes sampling efficiency by learning an environment and using it for actor learning. The environment is a model that considers a dynamic packet queue in a situation where a wireless channel is shared. We show the performance results of the proposed learning algorithm in the considered environment.
Distributed Optimization and Sampling with Constraints from Machine Learning

Optimization and sampling are fundamental techniques in machine learning. Recent development in learning theory has brought new insight to optimization and sampling on differentiable manifolds. In this paper, we focus on a special class of manifold that is the product of multiple manifolds, especially the unit spheres and hyperbolic spaces with Lorentz model. It has many real life applications, e.g., the head movement data of a robot can be represented on a torus, which is a product of two circles. On the other hand, the hyperbolic manifold, both the Poincaré disk model and Lorentz model, is widely used in word embedding. We state some basic properties of the distributed spherical gradient descent on the product of spheres and hyperbolic spaces, showing that the exponential map provides a unified framework on optimization and sampling on this special type of manifold.
Internal Parameters Extraction Algorithm for Mono and Polycrystalline Photovoltaic Panels Using Single-Diode Model

In this article, an algorithm for determining the five unknown parameters of the single diode model of a photovoltaic (PV) panel using only the catalog data is established and verified. To illustrate the performance of the proposed algorithm, the five parameters are determined from the catalog data of different commercial PV panels. With thus determined parameters, the current-voltage (I-V) characteristics of the considered PV panels are constructed and these characteristics are compared with the manufacturer experimental I-V characteristics of the considered panels. The results of the performed comparisons show a good concordance between the calculated characteristics and the experimentally determined ones.
Improving Online Teaching by Short-Time Activities

During the pandemic and the lockdown stage, online teaching based on information and communication technology (ICT) was a challenge for teachers and students. Even if we all have smart devices and Internet access almost everywhere, the problem was the missing feedback of the students, their reduced focusing power and, sometimes, the lack of motivation, even a boring feeling. This research work aims at planning the online teaching based on short time activities with high impact on maintaining the students’ interest in the studied content. Teachers and students must be familiar with the online learning technology. Diversity of the online teaching activities ensures that the students keep their concentration during classes. Competition between students and the possibility of self-evaluation motivate them to perform better and even to be the leader of the group. Time limits for the class activities imposes students to work efficiently and to outdo themselves. Creativity must be encouraged and stimulated by interesting teaching methods. Gaming is a good teaching option for the current and future generations of students as they are so different in comparison to previous other generation. Specially created games can offer the interactive environment for study and teaching. Teachers can use different software tools to develop this kind of activities dedicated to the lecture content. We exemplify some of these activities for online teaching in a Romanian class of Networking and Cybersecurity.
A Spatio-temporal Deep Learning Approach for Underwater Acoustic Signals Classification

Target recognition from underwater acoustic signals is a major challenge in surveillance systems, especially in military and defense fields. Deep learning models are increasingly used for the automatic classification of underwater signals, but many challenges remain due to the complexity of sound navigation and ranging networks, the noise present in the signals, and the difficulty of collecting large amounts of data for efficient training. In this paper, we propose two new architectures for underwater signal classification based on Spatio-temporal modeling. In experiments, evaluations on two real datasets show that the proposed approach achieves a classification accuracy of 98% which outperforms the state-of-the-art methods. In addition, the proposed end-to-end network is considerably faster than MFCC-based networks such as Yamnet and VGGish.