Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of the Local Sound Pressure on a Bias-Flow Liner Using High-Speed Holography and Tomographic Reconstruction

Bias-flow liners (BFL) are used as part of the cooling mechanism in airplane turbines and they also enable the reduction of noise emission. The attenuating principle is based on the interaction between sound waves and air flow. This turbulent aeroacoustic interaction occurs on a microscopic level and under harsh conditions. Therefore, in order to investigate local aeroacoustic phenomena, the distribution of the local sound field impedance, defined as sound pressure over acoustic particle velocity, is measured non-invasively. This work focuses on the non-invasive measurement of the local sound pressure. A fast camera-based holographic measurement system for the two dimensional tomographic measurement of the local sound pressure with high spatial resolution is presented. It features a spatial resolution of $52~\mu \text{m}$ with a simultaneous acquisition of up to 1600 measuring points. This enables the observation of sub-mm aeroacoustic effects, which are expected in the near field over the BFL’s surface, while reducing the measurement duration by three orders of magnitude compared to single point measurement systems. An experimental validation of the camera-based tomographic system is carried out by reference measurements. The system was applied at an acoustical model and an aeroacoustic model. The measured local sound pressure variations are discussed for both models. This work seeks to enable the non-invasive investigation of sound pressure, in order to further understand aeroacoustic effects, their sound attenuating properties and increase the attenuation capability of a BFL without degrading the overall performance of turbines.
The Local Delay and the Throughput in Cooperative Cognitive Radio Ad Hoc Networks

Cognitive radio ad hoc network is a framework which combines cognitive technique and ad hoc network. How to improve the performance of that network has always been a popular research for the past decades. In this paper, we study the local delay and the throughput in cooperative cognitive radio ad hoc networks. In order to forward the packets of primary users, a <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\tau }$ </tex-math></inline-formula>-slotted ALOHA protocol is adopted by secondary users in which a slot is divided into the first <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\tau }$ </tex-math></inline-formula>-slot and the latter <inline-formula> <tex-math notation="LaTeX">$\left ({\boldsymbol {1-\tau }}\right)$ </tex-math></inline-formula>-slot. By modeling the location of primary and secondary users as homogeneous Poisson point processes, we give the closed-form expression of the local delay and the throughput of both networks with two access strategies. Then, we optimize the two performance parameters with the intensity of secondary users. Numerical results show the feasibility of the optimal problem about the network performance metrics we proposed and could get an obvious better performance for primary users than that of ALOHA protocol by less sacrificing the performance of secondary users.
Prototype of an Ambulatory ECG Monitoring System with R Wave Detection in Real Time Based on FPGA

A prototype of an ambulatory ECG monitoring system was developed for the acquisition and storage of 3 simultaneous leads with an R wave detection algorithm based in the continuous spline wavelet transform to obtain the beat-to-beat heart rate in real time in 2 leads. The prototype has as core a 4 channel ECG front-end ADS1294, a Field Programmable Gate Array (FPGA) of Xilinx Artix-7 XC7A35T-ICPG236C in which the R wave detection algorithm was implemented, and a micro SD memory of 16 GB for data storage. The prototype has a bandwidth of 200 Hz, a minimum CMRR of 112 dB in 100 Hz, a resolution of $\mathbf{0.76}\ \mu \mathbf{V}$ and an average current consumption of 125 mA for a minimum duration of 30 h. Evaluation of the R wave detection algorithm in aVF lead has been done with 30 min records of 5 subjects at rest, with an accuracy of 99.4%.
Prosthetic Rehabilitation Training in Virtual Reality

This paper presents the design of a prototype system to allow users to experience the use of a prosthetic hand in a virtual environment using HTC Vive by employing the Serious Games 4 Health approach. The main objective of the work was to create an immersive and engaging tool that can be utilized during the rehabilitation process of a myoelectric prosthesis for an upper limb amputee. The rise of virtual reality technology presents an opportunity to implement a virtual rehabilitation training tool for transradial amputees. The control of the virtual hand is conducted through detection of electromyography signals of the upper limb using the Thalmic Labs Myo Gesture Control Armband. The prototype system developed demonstrates a successful proof of concept achieving the aims of the work whilst providing a platform for further research into the use of virtual reality for prosthetic rehabilitation.
Network behavior analysis of selected operating systems

The aim of this paper is to obtain characteristic behavior patterns of operating systems with a focus on the communication observed by a remote observer. Network communication of selected operating systems is observed, and the list of contacted targets is recorded. Based on the list, we try to identify the type of operating system used or a group of installed applications. Obtained data can be used for passive reconnaissance of remote networks, detection of possible undesirable leaks of information, filtering of selected communication, etc.
Accident Lane Prediction Using Probabilistic Inference

In this study a Bayesian Hierarchical Model for predicting the accident lane using the average section speed and density data obtained from a sensor or GPS was developed. With the assumption that speed and density values are normally distributed in an accident situation depending on the lane the accident has occurred, we have modelled the related parameters and predicted the probability of occurrence for each lane. This method can be used in real time traffic services where accidents are recorded by users or detected by other algorithms in order to alert the drivers about the lane of the accident. The awareness to the accident lane can ease the channeling of vehicles to the non-accident lanes and can help prevent the secondary accidents.
Rules of Navigation in River Bridge Waters Based on Ship Out-of Control Theory

Aiming at the complex hydrological environment and limited waterway in the river bridge waters, calculate the turbulence width of the pier as the safety distance between the ship and the pier. Calculate the dangerous area of the ship in the bridge waters according to the ship out-of control theory, and combined with the actual situation of the ship sailing in the river bridge waters, the navigation rules of ships in bridge waters are constructed. Through the analysis of the example, the correctness and usability of the above model are verified, which provides a theoretical basis for the real-time manipulation of the ship.
A Rewarding Framework for Multiplayer Serious Games based on Competitive Reinforcement

To promote a desired behavior in serious games, the rewarding mechanism is an important part of game design that can directly affect the game experience. In single player serious games, the rewarding system is generally performed by negative reinforcement (removing barriers for positive behaviors and creating barriers for negative behaviors) or positive reinforcement (providing facilities to progress in the game for positive behavior). Although research has shown the advantages of positive reinforcement compared to negative reinforcement, this issue is not well studied for multiplayer serious games. To address this problem, we propose a new rewarding framework, which is called competitive reinforcement rewarding. In this framework, a positive behavior of a player results in difficult conditions for other players competing in the game, while a negative behavior of a player facilitates the scoring process of opponents. We have applied this concept on a attention training game based on a multiplayer adversarial car racing game. We performed a comprehensive user study to evaluate the attention level, emotions and attitudes of players in competitive reinforcement versus positive/negative reinforcement versions. Our findings suggest that the competitive reinforcement rewarding framework provides more encouragement for players to increase and maintain their attention level than positive and negative reinforcement rewarding. We also showed that this rewarding framework can affect the emotional state and the behavior of players that can consequently improve their playing experience.
System Design for Early Fault Diagnosis of Machines using Vibration Features

The faults diagnosis of machines consists of feature extraction and classification of faults. The fault diagnosis process is based on the fact that each fault in a machine has a unique vibrational feature. Every industry wants a compact device or embedded system of low cost for the early faults diagnosis of machinery. This paper presents the platform that is focused at the embedded system design for the early faults diagnosis of machine(s) and classification of faults. We performed our experiment on the test rig apparatus and collected the vibration signals of four states of machine, those were: normal state, cracking state, offset pulley state and wear state. In segmentation, we use the empirical mode decomposition (EMD) technique. For classification purpose, we are using k-nearest neighbors (K-NN). Achievement of this research is that embedded system design for the classification of different faults in the machines. The overall accuracy of our experiment is 91.5%.
3D Body Image Perception and Pain Visualization Tool for Upper Limb Amputees

Upper limb amputation is often followed by a sensation that the affected limb is still present. This phantom limb representation is also reflected in the body image of the patient and can be associated with painful and non-painful sensations. Commonly, phantom limb appearance and pain is assessed with questionnaires, interviews and analogue drawings onto a 2D human outline. However, for patients it is hard to express the characteristics and sensations of the phantom limb and for clinicians it is challenging to compare and track patient testimonials. Our aim was to develop and evaluate a digital program to visualize phantom limb appearance and position, as well as pain and cramp hot spots of the phantom limb, on a 3D human model that represents the body image of the patient. Usability results show that a variety of upper limb impairments and pain areas can be adequately represented using this software. A 3D avatar is modeled after the patient’s body image and not only the appearance of the phantom limb itself can be depicted, but also its somatic abnormalities, such as telescoping. In addition, every dimension of the 3D model can be quantified, to not only provide a visual and qualitative analysis, but also quantifiable results for longitudinal analyses and comparison of different groups. Pain intensity and cramps can be drawn directly onto the rotatable 3D model to accurately reflect the patient’s perception. The 3D body image perception visualization tool CALA offers a novel and innovative computer-assisted method for the realistic representation of body, phantom limb and pain perception.
Software-Defined MANET Swarm for Mobile Monitoring in Hydropower Plants

The construction and running of hydropower plants does not merely involve hydropower generation itself but also facilities surveillance, water quality monitoring, harmful creatures tracking, etc., thus requiring mobile and collaborative monitoring capabilities. MANET (Mobile Ad Hoc Networks), with its mobility, flexibility, and robustness in volatile networking environment, is a competitive candidate to fulfill such tasks. Nevertheless its distributed structure prevents the effective collaboration between MANET nodes. SDN (Software-Defined Networking) provides the centralized control over the network underlay. This paper proposes the SDN-controlled IEEE 802.11 MANET swarm for mobile monitoring in hydropower plants. First, the MANET node is implemented by integrating Raspberry Pi with cameras, various sensors, etc., into the low-cost wheeled mobile hardware to enable sensibility and mobility. Then, multiple such MANET nodes are networked through ad hoc protocols, to construct a flexible and distributed MANET underlay; meanwhile, to implement centralized control over the MANET underlay, every MANET node is equipped with OpenFlow switch software (e.g., Open vSwitch) so that OpenFlow directives issued by the SDN controller can be understood and executed, hence the SDN overlay on top of the MANET underlay. Finally, OpenFlow is extended to offer physical actions such as mobility and sensibility manipulation, beyond pure data forwarding in traditional SDN applications, to achieve mobile monitoring in hydropower plants. The SDN-controlled MANET swarm features mobile and sensing capabilities, flexible networking through ad hoc protocols, and efficient and unified control by SDN. Experiment results prove the feasibility of this network architecture.
Towards Designing Pre-trip Traveler Information Systems: A Quantitative Evaluation

The progressing traffic collapse burdens the economy, the environment, and the traveler. In order to reduce the monetary, temporal, and individual costs produced by traffic, we need innovative multi-modal pre-trip information systems (IS). Without multi-modal pre-trip IS, planning individual mobility and selecting mobility options is still a pain point for many travelers. Hence, many travelers stick to habitual mobility behavior without weighing up different alternatives. Multi-modal solutions will, however, facilitate individual mobility planning by presenting alternatives proactively and thus reducing the cognitive load of modal and temporal choices. Since the integration of multi-modal mobility services in the everyday life of the end-user is still in its infancy stages, this paper focuses on the customer-centered development of multi-modal pre-trip IS. Thereby, this paper described the implementation and evaluation (n = 174) of pragmatic (i.e., information-based) and hedonic (i.e., pleasure-oriented) features into a smart mirror prototype. The results of our regression analysis shows that both pragmatic and hedonic qualities and the users’ experienced need fulfillment significantly predict the users’ intention to use the prototype. In conclusion, our implementation and evaluation contributes to the further development of multi-modal pre-trip IS. Our results contribute to our understanding of how to motivate travelers to use multi-modal mobility services and how we can reduce the travelers’ cognitive load during the mobility-centered decision-making process.
V2V- and V2X-Communication data within a distributed computing platform for adaptive radio channel modelling

This paper presents findings of the collection and exploitation process of V2X communication data with the aim of developing a measurement data-based radio channel model for the ITS frequency range around 5.9 GHz. Collected under real world conditions, connectivity quality measurements of ETSI ITS G5 communication data form the basis of the presented model prototype. The paper provides insight into the installation and configuration of the communication hardware used. Furthermore, the transmission process of accumulated as well as live data from the vehicles to a big data platform using the IoT message protocol MQTT is investigated. There, the communication data is enriched with other geographically referenced open source data. Finally, the development of a prototype V2X radio channel model using a machine learning process is presented. The model is a helpful instrument for predicting reception qualities in the ITS radio range for previously unknown receiver positions and thus a prerequisite for two exemplary presented use cases.
SAR Target Recognition Based on Cross-Domain and Cross-Task Transfer Learning

Inspired by their tremendous success in optical image detection and classification, convolutional neural networks (CNNs) have recently been used in synthetic aperture radar automatic target recognition (SAR-ATR). Although CNN-based methods can achieve excellent recognition performance, it is difficult to collect a large number of real SAR images available for training. In this paper, we introduce simulated SAR data to alleviate the problem of insufficient training data. To address domain shift and task transfer problems caused by differences between simulated and real data, we propose a model that integrates meta-learning and adversarial domain adaptation. We use sufficient simulated data and a few real data to pre-train the model. After fine-tuning, the pre-trained model can quickly adapt to new tasks in real data. Extensive experimental results obtained in the real SAR dataset demonstrate that our model effectively solves the cross-domain and cross-task transfer problem. Compared with conventional SAR-ATR methods, the proposed model can achieve better recognition performance with a small amount of training data.
Stress Classification System for Intelligent Wheelchair

This research has developed a stress detection system for the disabled person who uses a wheelchair based on physiological signals. Physiological signals that we used to detect stress are galvanic skins response (GSR) and heart rate (HR), these parameters were collected from BIOPAC. The GSR was detected by 2 sensors on fingertips at the left forefinger and middle finger. Heart rate was detected by 3 sensors one on the left wrist and two on the ankles. The video game was selected to stimulate stress from 7 subjects during 19–21 years old. The proposed method consists of several parts which are (i) the feature extraction by MAV, (ii) Classification by Support Vector Machine (SVM). The experiment results of our proposed method show that the system has stress detection rate 97.1 percent and non-stress detection rate 100 percent. The result is very promising.
Unobtrusive Behavioral Analysis of Students in Classroom Environment Using Non-Verbal Cues

Pervasive intelligent learning environments can be made more personalized by adapting the teaching strategies according to the students’ emotional and behavioral engagements. The students’ engagement analysis helps to foster those emotions and behavioral patterns that are beneficial to learning, thus improving the effectiveness of the teaching-learning process. Unobtrusive student engagement analysis is performed using the students’ non-verbal cues such as facial expressions, hand gestures, and body postures. Though there exist several techniques for classifying the engagement of a single student present in a single image frame, there are limited works on the students’ engagement analysis in a classroom environment. In this paper, we propose a convolutional neural network architecture for unobtrusive students’ engagement analysis using non-verbal cues. The proposed architecture is trained and tested on faces, hand gestures and body postures in the wild of more than 350 students present in a classroom environment, with each test image containing multiple students in a single image frame. The data annotation is performed using the gold standard study, and the annotators reliably agree with Cohen’s $\kappa = 0.43$ . We obtained 71% accuracy for the students’ engagement level classification. Further, a pre-test/post-test analysis was performed, and it was observed that there is a positive correlation between the students’ engagement and their test performance.
Multi-Mode Social Network Clustering via Non-Negative Tri-Matrix Factorization With Cluster Indicator Similarity Regularization

Community discovery algorithms are important aspects of network science, especially as social network structures become more complex. Multi-mode social networks have recently become a challenging and popular topic in this field. At present, inner-mode relationship is mainly considered in community discovery algorithms for social networks. Thus, the effect of the these methods is not well in clustering as the intra-mode relationship is not considered in the clustering methods. In this paper, we propose a flexible and robust clustering framework, MRTA (the Multi-Similarity Regular Tri-Factorization Algorithm), based on non-negative tri-matrix factorization. MRTA has several advantages over the existing methods. First, it achieves more consistent clustering results based on cluster indicator of inner-mode and intra-mode relationships of multi-mode networks. Second, it can simultaneously cluster multiple modes, which is impossible for single-mode clustering algorithms. Finally, it provides a multi-mode clustering solution that is more robust to noise. We perform an efficient iterative update algorithm, and theoretically prove its accuracy. Extensive experimental results of a variety of real and synthetic networks demonstrate the effectiveness of our approach.
Physiological Effect of Illuminance on Sense of Devotion during Video Gaming

Video gaming using smartphones and tablets becomes very popular among people of all ages. We evaluated the effect of illuminance on devotion during video gaming by using electroencephalogram. Fm theta rhythm is a very excellent measure for such purpose, because this rhythm can be recorded when a subject devotes his or herself to a task like a video gaming. We quantified the depth and duration of devotion by Fm theta rhythm index while a subject playing the famous video game, Tetris. The results showed the depth and duration of devotion are not influenced by illuminance.
A New Strategy to Design Microstrip Antenna Array With Low Side-Lobe Level and High Gain

A new strategy for designing low side-lobe level (SLL) antenna array is presented. This strategy utilizes special arrangement of antenna elements rather than direct tapered excitation amplitude to obtain an equivalent desired amplitude distribution in the main planes and thus achieve low SLL and high gain concurrently. To illustrate the strategy, 9-element microstrip antennas are arranged in four manners. An equivalent analysis method is also proposed to predict the SLL of the arrays. Using this method, a fast analysis of the trend of a microstrip array for a low SLL can be performed and thus a proper array-element arrangement is acquired. Both numerical and experimental results demonstrate that the SLLs in E and H planes of the novel array are fairly low. Meanwhile, the main beam of the novel array is almost the same with that of a conventional high-gain array. The antenna arrangement manner and the proposed strategy are particularly suitable for applications when low SLL and high gain are both demanded.