Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Five-Level Voltage Source Inverter: Modulation and Control

In this article, a new five-level voltage source inverter is proposed for high-power applications. The proposed inverter is competitive in performance, component count, and control complexity compared with the conventional multilevel inverters. Also, the proposed inverter has a simple structure as it does not have any dc-link neutral points, unlike hybrid converters. Furthermore, the proposed topology can be connected in a back-to-back due to the presence of a common dc-link. Also, there is no need of isolated dc source and a complex phase-shifting transformer. In addition, a simple voltage balancing approach based on a level-shifted carrier pulsewidth modulation scheme is proposed to control the flying capacitor voltages. The proposed approach will make use of the redundancy switching states to balance the flying capacitor voltages in the proposed inverter. The steady-state and transient performance of the proposed five-level voltage source inverter and voltage balancing approach is validated through MATLAB simulation studies at different power factors and modulation indices. The simulation studies are further evaluated experimentally by a scaled-down laboratory prototype. Furthermore, the feasibility of the proposed topology is analyzed under the performance indices of voltage and current harmonic distortion, flying capacitor voltage ripples, converter power losses, and converter efficiency.
A Pilot Study on Amblyopic Children for Possible Quantification of the Left/Right Mismatch

Amblyopia is a childhood eye condition with low vision in one or rarely both eyes, due to suppression at the visual cortex, that persists after rectification of risk factor at eye level. For an amblyopic child, the visual inputs from the amblyopic eye (AE) is suppressed at the brain. This leads to reduced visual acuity and poor or complete loss of stereopsis. Conventional clinical tests such as Worth 4-dot test and Bagolini striated lens test can only detect the presence of suppression but cannot quantify the extent of suppression, which is important for quantifying and identifying the effectiveness of treatments for amblyopia. In this paper, we propose a possible cost-effective and child-friendly method for quantifying the level of ocular suppression in amblyopia. The procedure is based on the fact that for amblyopic subjects, there is an asymmetry in the amount of ocular suppression experienced by the AE and this suppression leads to an inferior performance of the AE in an image recognition task. Preliminary studies performed on six each of control and amblyopic subjects are presented in this paper. We have shown that the absolute value of the deviation of the ratio of accuracies of both eyes in an image recognition task from unity can be used as a measure of the suppression. Paired t-test revealed a significant difference between the means of the accuracies of amblyopic and fellow eyes (p=0.03) in the case of amblyopic subjects. Equivalence test done using ‘two-one-sided t-tests’ procedure shows that the equivalence of the accuracies of left and right eyes for control is statistically significant (p = 0.008, symmetric equivalence margin of 5 percentage points).
Error-Aware SCFlip Decoding of Polar Codes

The successive-cancellation flip (SCFlip) decoder and its variants provide a significant coding gain with the average complexity practically identical to that of the successive cancellation (SC) decoder in a wide range of signal-to-noise ratios (SNRs). But, they suffer from high complexity and long latency when the SNR decreases, since the average number of extra decoding attempts becomes inevitably large. To mitigate this problem, we propose a novel SCFlip decoder, called an error-aware SCFlip (EA-SCFlip) decoder, for distributed cyclic-redundancy-check (CRC) polar codes. Based on the distributed CRC bits, it employs early termination at each extra decoding attempt so that it can reduce the decoding complexity and latency on the average. It also reduces the search space of candidate bit-flips in the dynamic building of the bit-flip list by exploiting the parity-check relationship (PCR) of the first error-detected CRC bit at each extra decoding attempt. Furthermore, we propose a greedy algorithm to design a distributed CRC code such that the obtained PCRs make the early-error-detection capability of the EA-SCFlip decoder as high as possible. Numerical results demonstrate that the EA-SCFlip decoder can indeed achieve an early termination gain as well as a complexity reduction, when a polar code is concatenated with the distributed CRC code designed by the proposed algorithm.
Quality-Aware Neural Adaptive Video Streaming With Lifelong Imitation Learning

Existing Adaptive Bitrate (ABR) algorithms pick future video chunks’ bitrates via fixed rules or offline trained models to ensure good quality of experience (QoE) for Internet video. Nevertheless, data analysis demonstrates that a good ABR algorithm is required to continually and fast update for adapting itself to time-varying network conditions. Therefore, we propose Comyco, a video quality-aware learning-based ABR approach that enormously improves recent schemes by i) picking the chunk with higher perceptual video qualities rather than video bitrates; ii) training the policy via imitating expert trajectories given by the expert strategy; iii) employing the lifelong learning method to continually train the model w.r. t the fresh trace collected by the users. To achieve this, we develop a complete quality-aware lifelong imitation learning-based ABR system, construct quality-based neural network architecture, collect a quality-driven video dataset, and estimate QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate Comyco reaches 1700-fold improvements in the number of samples required and 16-fold speedup in the training time compared with the prior work. Meanwhile, Comyco outperforms existing methods, with the improvements on average QoE of 7.5%–16.79%. Moreover, experimental results on continual training also illustrate that lifelong learning helps Comyco further improve the average QoE of 1.07%–9.81% in comparison to the offline trained model.
Design Principle for Linear Electrical Machines to Minimize Power Loss in Periodic Motions

This article describes a novel approach to the design of linear machines that are not run at a steady-state operating point. The focus lies on minimizing the power loss to achieve highest possible efficiency. First analytical considerations reveal the potential of designing electrical machines and spring mechanisms for periodic, accelerated motion. It shows the influence of the machine size on the power losses due to the dependence of machine force as well as moving mass on this parameter. Based on these findings, a simulation model was created that can be used with an optimization algorithm. The simulation consists of a stationary and a time-dependent finite-element analysis of the linear machine in conjunction with an operational model that includes a given movement cycle. It was used to design a flat-type linear machine for a free-piston linear generator. The simulation results validate the analytical calculations and illustrate the differences to conventional design approaches for linear machines.
Efficient Communication in Large Multi-robot Networks

To achieve coordination in a multi-robot system, the robots typically resort to some form of communication among each other. In most of the multi-robot coordination frameworks, high-level coordination strategies are studied but ‘how’ the ground-level communication takes place, is assumed to be taken care of by another program. In this paper, we study the communication routing problem for large multi-robot systems where the robots have limited communication ranges. The objective is to send a message from a robot to another in the network, routed through a low number of other robots. To this end, we propose a communication model between any pair of robots using peer-to-peer radio communication. Our proposed model is generic to any type of message and guarantees a low hop routing between any pair of robots in this network. These help the robots to exchange large messages (e.g., multi-spectral images) in a short amount of time. Results show that our proposed approach easily scales up to 1000 robots while drastically reducing the space complexity for maintaining the network information.
Murmur Detection in PCG signals using DWT Entropy and Feature Clustering

Phonocardiogram (PCG) signals, their feature extraction and segmentation to diagnose several heart disorders and developing detection models is the primary area of research. Multiple challenges are associated with the recording and preprocessing of these data due to inherent environmental noise and subject wise variation in heart sound. In this work two different approaches based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) is proposed and algorithms are developed to classify the normal subjects and the subjects having PCG signal with high level of murmur due to heart disorders. The results are finally processed through cluster-based analysis of the DWT components entropy value to discriminate the normal subjects and murmur inhibited PCG data.
Classical PI Controllers with Anti-Windup Techniques Applied on Level Systems: An Interesting Case Study

Systems which present the actuator element saturation for a long time can force high overshoot peaks due to the integral action. Considering that, the main objective of this work is to carry out a case study of an Anti-Windup Technique (AWT) with 5 distinct classic Proportional-Integral (PI) controllers applied on a SMAR didactic level system, where the results with and without AWT are compared to each one. The tests were carried out through implementations in MATLAB software, where the results showed a high necessity to use anti-windup techniques in these systems. As for the controller’s tuning method efficiency, the Integral of Squared Error (ISE) index proved that the Cohen-Coon and Ziegler-Nichols controllers showed better results.
An Enhanced Authentication Protocol based Group for Vehicular Communications over 5G Networks

Intelligent Transportation Systems (ITS) are applications that aim to provide innovative services in different modes of transport and traffic management. They allow users to be better informed about road conditions, and make the use of transport networks safer, more coordinated and smarter. Vehicular Ad hoc Networks (VANETs) are a key component of intelligent transportation systems. They allow users to have information on their route, weather, road conditions, and Internet access, all in real time. These networks also provide access to various relaxation and entertainment applications such as transporting multimedia data, obtaining tourist information. The VANET network provides access to information in real time and in private. This fact has a great impact on the behavior of the user, his life and that of the passengers. This network also ensures user security by preserving anonymity and securing their personal data. Several VANET safety challenges are considered by the existing vehicular network security standards. However, given the advancement of Fifth-Generation (5G) networks and the requirement for a variety of different applications and services across vehicular networks, merging 5G and vehicular ad-hoc networks is crucial. Nevertheless, as the immense amount of vehicles growing daily and each one of them needs to execute a complete network authentication process to join the network, there may be network congestion when a group of vehicles requires simultaneous access, and may also have a major impact on the high rate of 5G. The Authentication and Key Agreement (AKA) mechanism introduced by the Third-Generation Partnership Project (3GPP) in Fourth-Generation (4G) and 5G networks is inadequate to manage many devices. The purpose of this work is to propose an enhanced and secure authentication and key agreement protocol based group of vehicles is 5G networks. The security analysis verified by Automated Validation of Internet Security Protocols and Applications (AVISPA) tool has demonstrated that our solution achieves the security and privacy objectives. In addition, the performance evaluations in terms of communication and computational overheads illustrate that our protocol outperforms several existing protocols.
A Framework for Interactive Virtual Fixture Generation for Shared Teleoperation in Unstructured Environments

Virtual fixtures (VFs) improve human operator performance in teleoperation scenarios. However, the generation of VFs is challenging, especially in unstructured environments. In this work, we introduce a framework for the interactive generation of VF. The method is based on the observation that a human can easily understand just by looking at the remote environment which VF could help in task execution. We propose a user interface that detects features on camera images and permits interactive selection of the features. We demonstrate how the feature selection can be used for designing VF, providing 6-DOF haptic feedback. In order to make the proposed framework more generally applicable to a wider variety of applications, we formalize the process of virtual fixture generation (VFG) into the specification of features, geometric primitives, and constraints. The framework can be extended further by the introduction of additional components. Through the human subject study, we demonstrate the proposed framework is intuitive, easy to use while effective, especially for performing hard contact tasks.
Learning Shape-based Representation for Visual Localization in Extremely Changing Conditions

Visual localization is an important task for applications such as navigation and augmented reality, but is a challenging problem when there are changes in scene appearances through day, seasons, or environments. In this paper, we present a convolutional neural network (CNN)-based approach for visual localization across normal to drastic appearance variations such as pre- and post-disaster cases. Our approach aims to address two key challenges: (1) to reduce the biases based on scene textures as in traditional CNNs, our model learns a shape-based representation by training on stylized images; (2) to make the model robust against layout changes, our approach uses the estimated dominant planes of query images as approximate scene coordinates. Our method is evaluated on various scenes including a simulated disaster dataset to demonstrate the effectiveness of our method in significant changes of scene layout. Experimental results show that our method provides reliable camera pose predictions in various changing conditions.
Adversarial Feature Disentanglement for Place Recognition Across Changing Appearance

When robots move autonomously for long-term, varied appearance such as the transition from day to night and seasonal variation brings challenges to visual place recognition. Defining an appearance condition (e.g. a season, a kind of weather) as a domain, we consider that the desired representation for place recognition (i) should be domain-unrelated so that images from different time can be matched regardless of varied appearance, (ii) should be learned in a self-supervised manner without the need of massive manually labeled data, and (iii) should be able to train among multiple domains in one model to keep limited model complexity. This paper sets to find domain-unrelated features across extremely changing appearance, which can be used as image descriptors to match between images collected at different conditions. We propose to use the adversarial network to disentangle domain-unrelated and domain-related features, which are named place and appearance features respectively. During training, only domain information is needed without requiring manually aligned image sequences. Experiments demonstrated that our method can disentangle place and appearance features in both toy case and images from the real world, and the place feature is qualified in place recognition tasks under different appearance conditions. The proposed network is also adaptable to multiple domains without increasing model capacity and shows favorable generalization.
Semantic Segmentation of Litchi Branches Using DeepLabV3+ Model

Litchi is often harvested by clamping and cutting the branches, which are small and can easily be damaged by the picking robot. Therefore, the detection of litchi branches is particularly significant. In this article, an fully convolutional neural network-based semantic segmentation algorithm is proposed to semantically segment the litchi branches. First, the DeepLabV3+ semantic segmentation model is combined with the Xception depth separable convolution feature. Second, transfer learning and data enhancement are used to accelerate the convergence and improve the robustness of the model. Third, a coding and a decoding structure are adopted to reduce the number of network parameters. The decoding structure uses upsampling and the shallow features to fuse, and the same weight is assigned to ensure that the shallow feature semantics and the deep feature semantics are evenly distributed. Fourth, using atrous spatial pyramid pooling, we can better extract the semantic pixel position information without increasing the number of weight parameters. Finally, different sizes of hole convolution are used to ensure the prediction accuracy of small targets. Experiment results demonstrated that the DeepLabV3+ model using the Xception_65 feature extraction network obtained the best results, achieving a mean intersection over union (MIoU) of 0.765, which is 0.144 higher than the MIoU of 0.621 of the original DeepLabV3+ model. Meanwhile, the DeepLabV3+ model using the Xception_65 network has greater robustness, far exceeding the PSPNet_101 and ICNet in detection accuracy. The aforementioned results indicated that the proposed model produced better detection results. It can provide powerful technical support for the gripper picking robot to find fruit branches and provide a new solution for the problem of aim detection and recognition in agricultural automation.
Automated detection of soleus concentric contraction in variable gait conditions for improved exosuit control

Exosuits can reduce metabolic demand and improve gait. Controllers explicitly derived from biological mechanisms that reflect the user's joint or muscle dynamics should in theory allow for individualized assistance and enable adaptation to changing gait. With the goal of developing an exosuit control strategy based on muscle power, we present an approach for estimating, at real time rates, when the soleus muscle begins to generate positive power. A low-profile ultrasound system recorded B-mode images of the soleus in walking individuals. An automated routine using optical flow segmented the data to a normalized gait cycle and estimated the onset of concentric contraction at real-time rates (~130Hz). Segmentation error was within 1% of the gait cycle compared to using ground reaction forces. Estimation of onset of concentric contraction had a high correlation (R2=0.92) and an RMSE of 2.6% gait cycle relative to manual estimation. We demonstrated the ability to estimate the onset of concentric contraction during fixed speed walking in healthy individuals that ranged from 39.3% to 45.8% of the gait cycle and feasibility in two persons post-stroke walking at comfortable walking speed. We also showed the ability to measure a shift in onset timing to 7% earlier when the biological system adapts from level to incline walking. Finally, we provided an initial evaluation for how the onset of concentric contraction might be used to inform exosuit control in level and incline walking.
Preliminary Study of an Aerial Manipulator with Elastic Suspension

This paper presents a preliminary study of an Aerial Manipulator suspended by a spring to a robotic carrier. The suspended aerial manipulator is actuated by six pairs of contra-rotating propellers generating a 6-DoF wrench. Simulations show path following results using a computed torque (feedback linearization) control strategy. Active vibration canceling is validated experimentally on a first prototype.
Digital Twin Based Software Design in eHealth - A New Development Approach for Health / Medical Software Products

The already presented digital twin concept for information management of health and medical software products is further elaborated with respect to regulatory requirements, the FDA and EU unique device identification (UDI) system, and software product lifecycle management. The concept builds on (1) a personal digital twin as a Gemini of the patient, (2) a group digital twin modelling the designated user group of the software, and (3) a system digital twin for the software product itself. Beside the elaboration of the digital twin concept, we present the strengths and limitations of a digital twin based software design and development approach for health and medical software. The approach deliberately entails a better support of agile development techniques in comparison to the V-model based classic software development in this field of application.
Novel PrNd-Lean Ce-Based R₂Fe₁₄B Permanent Magnets With High Performance

In this article, the phase compositions, microstructures, and magnetic properties of PrNd-free Ce-Fe-B-based sintered magnets with the nominal composition of (Ce<sub>0.78</sub>Y<sub>0.17</sub>La<sub>0.05</sub>)<sub>16.28</sub>Fe<sub>bal. </sub>B<sub>7.44</sub>M<sub>1.94</sub> (at.%: M = Co, Al, Cu, and Nb) have been investigated. It shows that a lamellar crystalline structure is formed in the Ce-Fe-B-based strip casting (SC) alloy. The Y-substituted Ce-Fe-B-based SC alloy is mainly composed of R<sub>2</sub>Fe<sub>14</sub>B and RFe<sub>2</sub> phases. It suggests that Y substitution is helpful for depressing the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-Fe phase generated in the SC alloy. The island-like intermediate phases exist in the RFe<sub>2</sub> region, which are probably caused by the incomplete peritectic reaction during alloy solidification. The Curie temperature <inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {c}}$ </tex-math></inline-formula> of the Y-substituted Ce-Fe-B-based alloy is about 463 K, which is higher than that of the Ce<sub>2</sub>Fe<sub>14</sub>B compound (<inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {c}} =424$ </tex-math></inline-formula> K). For the experimental Ce-Fe-B-based sintered magnet without PrNd addition, the remanence <inline-formula> <tex-math notation="LaTeX">$B_{\mathrm {r}}$ </tex-math></inline-formula> of 8.31 kG, the coercivity <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {cj}}$ </tex-math></inline-formula> of 0.92 kOe, and the maximum energy product (BH)<sub>max</sub> of 5.30 MGOe have been obtained. A good combination of coercivity <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {cj}}$ </tex-math></inline-formula> (= 5.57 kOe) and maximum energy product (BH)<sub>max</sub> (= 20.17 MGOe) has been achieved in the Ce-Fe-B-based blended magnet with PrNd/<inline-formula> <tex-math notation="LaTeX">$\sum $ </tex-math></inline-formula>RE = 25% prepared using a binary main phase (BMP) method. The heterogeneous elemental distributions and microstructure characteristics of the BMP magnet are also analyzed.