Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Smoke Detection Method with Long Short-Term Memory Network

Since smoke does not have inherent color and shape features, conventional image analysis methods for object detection do not work well as they are expected. Therefore, feature extraction for smoke detection tends to be difficult. In addition, image information of smoke has a large variation due to its transparency and it is heavily affected from its background environment. To address these problems, this paper presents a block-based smoke detection method using a machine learning approach. In this method, a gray-scale block-based smoke region discrimination is presented based on the recurrent neural network with Long Short-Term Memory (LSTM). An image sequence is treated as a block-wise time series and the LSTM network discriminates blocks which include smoke region by the motion of smoke regions. The LSTM is expected to handle a large variation of smoke information caused from smoke's properties. In the presented method, image sequence is divided into fixed-size blocks which are treated as time series data and the LSTM is trained to discriminate whether each block contains smoke. The presented method is evaluated by several image sequences and training conditions and it is confirmed that LSTM based method can work well to improve smoke detection results.
Disturbance-Observer Based Tracking Control of Industrial SCARA Robot Manipulators

This paper presents a new feedback-feedforward tracking control method for serial manipulators in presence of modeling uncertainties, and both known and unknown disturbances. To improve the tracking performance, the proposed scheme is composed of three main control components: feedforward control, disturbance-observer-based control and feedback control. The feedforward action is designed to account for the effects of the reference signal, considered as known disturbance, on the tracking error dynamics. The disturbance-observer-based control law is proposed to deal with the modeling uncertainties as well as unknown disturbances. Using the concepts of $V$-stability and H∞ control, the feedback component is designed to satisfy some predefined closed-loop specifications, aiming to improve the tracking performance. The effectiveness of the new tracking control method is demonstrated with a multibody model of a 2-DoF SCARA robot manipulator. In particular, a comparison with two classical tracking control methods is performed to emphasize the interest of our method for robotics control.
Coherent Estimation of Range Difference and Range Rate Difference for Unknown Frequency Hopping Signals

This paper addresses the estimation problem of range difference and range rate difference for unknown frequency hopping signals in passive emitter localization, considering the range migration (RM) of the high-speed moving target within the observation time. A coherent estimation algorithm based on the scaled Fourier transform is proposed. This method can effectively remove the RM and random phase effects. The whole estimation process can be fast implemented without any searching operation. Numerical experiments demonstrate that the estimation performance of proposed algorithm is superior to existing algorithms, and comparable to the maximum likelihood estimator.
STFANET: SDN-Based Topology Management for Flying Ad Hoc Network

In recent years, with the growth in the use of Unmanned Aerial Vehicles (UAVs), UAV-based systems have become popular in both military and civil applications. The lack of reliable communication infrastructure in these scenarios has motivated the use of UAVs to establish a network as flying nodes, also known as Flying Ad Hoc Networks (FANETs). However, the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology, which makes more challenging to guarantee their communication during the operational time. In this context, this article presents a Software-defined networking (SDN) based Topology management for FANETs - called of STFANET -, which is a coordination protocol that englobes both an efficient SDN-based UAV communication and a set of topology management algorithms. The goal is to establish and maintain a FANET topology in order to provide a constant and reliable communication link among independent nodes - which are performing individual or collaborative missions - through relays units. Simulation results show the efficiency of the proposed protocol in order to provide communication in a dynamic scenario. Considering its use in a military setting, STFANET managed to achieve 25% of packet loss in transmitting data packets, 1.5ms of latency and 71% of connectivity on average.
Experimental Analysis of Three-Phase Induction Motors with Multiflux, Dual-Winding Configurations

In this paper, several multiflux, three-phase induction motors, dual-winding configurations are experimentally analysed. Using a motor with two reconfigurable stator windings sharing the same slots, several dual-winding configurations with different spatial displacement between the two partial windings, are experimentally tested. A programmable voltage source is used to adjust the line-to-line voltage at the terminals of the motor so that the fundamental flux level is the same for all the configurations. Then, the experimentally determined value is compared to the calculated value of the electromotive force and the per-unit values of line-to-line voltage per flux or current per flux are measured and compared to simulated results. Finally, the locked-rotor impedance was measured in order to assess its variation with the fundamental winding factor.
Augmenting the audio-based expression modality of a non-affective robot

This paper investigates the potential benefits of augmenting audio-based affective means of expression to strengthen the perceived intentions of a robot. Robots are often viewed as being simple machines with limied capabilities of communication. Changing how a robot is perceived, towards a more affective interpretation ofits intentions, requires careful consideration of the means of expression available to the robot. It also requires alignment between these means to ensure they work in coordination with each other to make the robot easier to understand. As an effort to strengthen the affective interpretation of a soft robotic arm robot, we altered its overall expression by changing the available audio-based expression modalities. The system mitigatedthe naturally occurring noise from actuators and pneumatic systems and used a custom sound that supported the movement of the robot. The robot was tested by interacting with human observers (n=78) and was perceived as being significantly more curious, happy and less angry when augmented by audio that aligned with the naturally occurred robot sounds. The results show that the audio-based expression modality of robots is a valuable communication tool to consider augmenting when designing robots that convey affective information.
A BP-NN Decoding Algorithm for Polar Codes

Polar code is selected for the 5-th generation of wireless communication standard. Meanwhile, with the demand of low latency in 5G and great success in deep learning area, neural network decoder (NND) becomes a promising candidate for future decoder. However, NND is currently only feasible for very short code lengths. In this paper, we proposed a belief propagation-neural network (BP-NN) algorithm for polar codes based on partitioning. The core of our algorithm is replacing subblock of belief propagation (BP) decoder with NNDs, and using BP decoding framework to connect them. In addition, we also introduce a weight function to complete the message propagation process. Simulation results show the proposed algorithm reduces the decoding delay compared to the traditional BP algorithm and have a well bit error rate (BER) performance. At the same time, it also suitable for long polar codes.
Design of Rectangular Antenna Array Structure for Massive MIMO

As a key technology for 5G and beyond mobile communications, massive MIMO has been implemented, tested and will be widely deployed soon. Rectangular antenna arrays, associated with 32 to 64 transceivers, are the most commonly-considered configurations for massive MIMO base stations. When testing such massive MIMOsystems in practical environments, we often obtain lower-than-expected system performance. That is to say, performance gain does not keep up with increases in hardware complexity and cost, making massive MIMO cost-inefficient in practice. We approach this problem by adapting massive MIMO antenna array design to propagation characteristics in different deployment scenarios. In this paper, we focus on rectangular array structure design with practical limits of array dimensions. The 3GPP 3D channel model is used to extract propagation properties and evaluate corresponding performance of designed arrays. Simulation results show that we gain significant performance improvements by matching array structures to propagation characteristics, as compared to using conventional array configurations. More importantly, relatively small number of antennas/transceivers and very simple processing such as matched-filtering achieve this performance gain. The results indicate a potential to boost massive MIMO performance and to some extent solve the cost-inefficiency problem, by deploying antenna arrays with suitable structures for different propagation scenarios.
Comparative Analysis of Reconfiguration Assisted Management of Battery Storage Systems

Present battery storage systems are connected as fixed cell topology. Performance is limited due to the differences in ageing of different cells and failure situations. Static battery management systems and balancing methods with fixed cell topologies are developed to monitor and balance the cells, unfortunately, current designs are not able to mitigate the issues very well. Several reconfiguration designs have been proposed to not only monitor the cells but also control the connections between the cells which are not clearly worked out. In this paper, we have presented a generic reconfigurable battery topology which has the ability to monitor and configure the cells using appropriate switching to increase the lifetime of battery cells. An additional component is used in this topology which is named as switching network. The working principle and a techno-economic analysis of the switching circuit are developed to find the balance between the performance and the associated losses in the network. We also have compared the proposed topology with the existing static BMS architectures with passive and active balancing designs using a set of performance parameters-flexibility, reliability, management complexity, efficiency and cost.
A Flexible Hybrid Selective Harmonic Elimination Transition Algorithm to Provide Variable Frequency of Output Voltage in 3L-NPC Inverter

A versatile hybrid pulse width modulation (HPWM) scheme, which can combine individual characteristics of diverse PWM techniques and provide variable frequency of output voltage, is proposed for three-level neutral point clamped (3L-NPC) inverter based on change in pattern signal. In the presented HPWM, various switching patterns of selective harmonic elimination pulse width modulation (SHEPWM) are applied to adapt to each changed pattern, which varies according to the changed frequency. Moreover, one fast and smooth transition strategy is also investigated between these SHEPWM switching patterns, considering the aspects of reliability, efficiency and safety of the switched system. Finally the simulation results acquired through MATLAB/SIMULINK platform are used to validate and evaluate the feasibility and performance of the whole control system.
SDR Implementation of an End-to-End mmWave Testbed Based on Phased Antenna Array

The millimeter wave (mmWave)has become one of the key technologies in the fifth generation (5G)cellular network due to its potential in providing huge undeveloped bandwidths and increasing the communication capacity. Combining with beamforming based on large scale antenna arrays, mmWave communication shows great prospects for application. In this paper, we design and implement a 5G New Radio (NR)based end-to-end mmWave testbed, using software-defined radios (SDR)as baseband processing units and phased antenna arrays as beamforming devices. We first introduce the hardware architecture as well as the devices adopted in the testbed. Then, detailed physical layer design and implementation according to 5G NR standard are provided. We also design a beam codebook containing 64 beam patterns. Further, beam management module for aligning the transmitting and receiving beams is implemented based on the designed beam codebook. The experimental results validate the good performance of the mmWave testbed and demonstrate the feasibility of phased antenna array for beamforming.
3D Patch-Based Sparse Learning for Style Feature Extraction

How to extract features of different shapes for style similarity evaluation is a very challenging research topic. Different from methods based on predefined style templates, we perform sparse learning and triplet embedding directly from the 3D shape local descriptors. The proposed method can adaptively generate element-level style features of different 3D patches for style similarity evaluation. The proposed algorithm mainly consists of the following steps. First, given a heterogeneous 3D shape collection, we learn mid-level style features by Siamese networks based on curvature-guided sampling directly on 3D shapes, which is a view-independent and part-independent approach. Second, we perform sparse coding to reduce the dimension of mid-level local features and achieve more compact and discriminative style features, named element-level features. Finally, we enhance our element-level style features by triplet embedding learning over crowdsourced triplets, which maps similar examples close to each other and dissimilar examples farther apart. The proposed algorithm can aggregate different local features into a global style feature representation and shows satisfactory discriminability. Our method can avoid the loss of style features from the 3D model to the views and can learn more discriminative style features by sparse learning. The experimental results show that our sparse style feature representation significantly improves the accuracy compared with recent state-of-the-art algorithms.
Ultrasound shear wave elastography for assessing diaphragm function within the intensive care unit

Availability of robust non-invasive tools for assessing diaphragm function is clinically relevant in a variety of clinical settings. Diaphragm shear modulus (SMdi) assessed by using ultrasound (US) shear wave elastography (SWE) can be used as a surrogate of transdiaphragmatic pressure (Pdi) that is considered as the most specific approach to assess diaphragm function. Here, we investigated the ability of SWE to reveal changes in transdiaphragmatic pressure for assessing diaphragm function within an ICU setup. SWE was performed in 22 mechanically ventilated patients within varying breathing conditions to induce changes in Pdi. Eight patients displayed a significant correlation between changes in Pdi (ΔPdi) and changes in SMdi (ΔSMdi). An absence of correlation was associated with a significantly higher respiratory rate. These findings highlight the need to increase SWE sampling rate to improve the applicability of SWE for diaphragm monitoring.
Investigating Positive Psychology Principles in Affective Robotics

Positive emotions play a fundamental role in promoting well-being, social bonding, and encouraging people to flourish. We investigated an affective robot's potential to promote positive moods in human groups during a collaborative task. A between-subject experiment (N=39 teams, 78 participants) was conducted to compare the improvement in participants' mood and robot's impression after conducting a collaborative task with an affective robot showing either positive or neutral behaviors. We found that self-reported valence and arousal increased in human participants when interacting with the affective robot regardless of the robot's perceived mood. Additionally, we discovered that participants' likeability of the robot increased when interacting with a positive robot, while likeability decreased when interacting with a neutral robot. These results suggest that even if the evidence for emotional contagion between an affective robot and human group members is not conclusive, participants felt more positive after interacting with robots showing affective behaviors in human-robot team interactions.
A MAGIC population as an approach to the conservation and development of genetic diversity of winter barley for breeding purposes by on-farm management

Additional genetic diversity in crop breeding programmes and in crop production is required to better cope with the impact of climate change. To complement ongoing activities of winter barley breeders, an evolutionary plant breeding programme has been initiated in 2008. Out of 227 German winter barley varieties released between 1914 and 2003, a set of 58 varieties was genetically analysed using SSR markers. Among these, 32 genotypes representing the genetic diversity of the whole set were crossed according to the Multi-parent Advanced Generation Inter-Cross scheme (MAGIC) over six generations. The resulting 324 lines were combined to form a heterogeneous MAGIC winter barley population. In order to adapt to regional agricultural conditions, MAGIC subpopulations are currently being cultivated within a network of 12 eco-geographically contrasting locations and subjected to natural site-related selection as evolutionary bulks. Cultivation and seed saving will take place over a period of 6 to 8 years under high and low input production conditions. An information system named ROBUSTUM has been developed for consistent recording of cultivation conditions, of characterisation and evaluation data as well as for documentation of the pedigree and for subsequent data analyses. The present approach enables a continuous and dynamic adaptation of the natural genetic diversity present in our crops to climatic and site-related agronomic changes. This makes the evolutionary bulk particularly suitable for organic cultivation, but also for marginal sites and locations with unfavourable soil and weather conditions. In view of increasingly extreme climatic conditions, these properties may become even more important for agriculture in the future. Populations also contribute to the maintenance and development of a genetic diversity as broad as possible. This approach enables estimation of the potential of evolutionary bulks to contribute to the development of sustainable agricultural production systems.
Self-shape estimation algorithm for flexible ultrasonic transducer array probe by minimizing entropy of reconstructed image

Flexible ultrasonic transducer array probe can realize successive monitoring by pasting it to the body. Imaging with a flexible ultrasonic probe requires information of the elements’ relative position. In this paper, we propose an algorithm to estimate the array shape from radio frequency data acquired by synthetic aperture. With this algorithm, the array shape is estimated based on the entropy of the reconstructed image using the assumed array shape. Entropy is used to evaluate the sharpness of an image. The real array shape can be estimated by searching for the shape which minimizes the entropy. Simulation experiments to verify the proposed algorithm were conducted. A 16 elements, 5 MHz, 1mm pitch flexible transducer array was used. The average position error of all elements of 4 trials between the real shape and the estimated shape was 0.037 mm. These results indicate that the proposed algorithm has a possibility to enable medical imaging with flexible ultrasonic transducer array probe.
Day-Ahead Hierarchical Optimal Scheduling for Offshore Integrated Electricity-Gas-Heat Energy System Considering Load Forecasting

In order to ensure the economic operation of the offshore integrated energy system and improve the wind power consumption, a day-ahead hierarchical scheduling strategy should be formulated. Firstly, a short-term load forecasting model with interval time series auto-regressive integrated moving average method is built. Secondly, take the total cost as the multi-energy control layer and take the wind power curtailment cost as the energy efficiency optimization layer to establish the hierarchical optimal model. And a modified particle swarm optimization algorithm is used to obtain the optimal solution. Then two schemes are proposed to explore the impact on system. Finally, a certain offshore platform in China is taken as an example to calculate. The results show that the proposed method can realize the optimal scheduling of the system. And the application of power to gas technology can better accommodate wind power and greatly reduce the economic cost.
A stress recognition system using HRV parameters and machine learning techniques

In this study, we investigate reliable heart rate variability (HRV) parameters in order to recognize stress. An experiment protocol was established including different stressors which correspond to a range of everyday life conditions. A personalized baseline was formulated for each participant in order to eliminate inter-subject variability and to normalize data providing a common reference for the whole dataset. The extracted HRV features were transformed accordingly using the pairwise transformation in order to take into account the personalized baseline of each phase in constructing the stress model. The most robust features were selected using the minimum Redundancy Maximum Relevance (mRMR) selection algorithm. The selected features fed machine learning systems achieving a classification accuracy of 84.4% using 10-fold cross-validation.
Hybrid Precoding for Massive MIMO Systems Using Partially-Connected Phase Shifter Network

In recent years, there have been extensive researches on hybrid precoding for massive MIMO to achieve huge spectral efficiency with reasonable cost and power consumption. In this paper we consider a partially-connected phase shifter network (PSN) scenario where each antenna is only connected to one radio frequency (RF) chain, leading to much reduced hardware complexity. We then propose a new algorithm for maximizing the spectral efficiency given the hardware constraints, which we refer to as the universal hybrid precoding (UHP) algorithm for partially-connected (PC) network, or UHP-PC. Compared with the existing work, the UHP-PC algorithm is advantageous in that i) it has a significantly better performance than state-of-the-art and ii) it can deal with variable phase shifters with finite or infinite resolutions. The simulation results verify that using the proposed algorithm the performance loss of a partially-connected PSN compared to the (unrealistic) fully-digital system is moderate, despite the vastly reduced hardware complexity.
Handover Procedure Design and Performance Optimization Strategy in LEO-HAP System

High altitude platform (HAP) is an important supplement facility for low earth orbit mobile satellite system (LEO-MSS) to increase the capacity in hot-spot regions beyond the coverage of terrestrial networks. Because of the large propagation delay and the frequent passive handover caused by the quick movement of LEO satelliets, handover protocols and strategies should be studied and improved. In this paper, we design different handover procedures suitable for LEO-HAP system based on 5G protocol and analyze the performance on delay and signalling cost. And we propose a dynamic handover strategy to optimize the handover moment and resource allocation. Lagrange duality is used and a sub-gradient algorithm is presented to solve this problem. Simulation results show that the proposed handover processes provide better performance on delay and signalling cost compared with the traditional protocol. And the proposed strategy has an excellent performance on drop rate and power consumption.