Miracle-dz/UniFAR-FTUs huggingface dataset:
Cable-Driven 4-DOF Upper Limb Rehabilitation Robot

This paper developed a 4-degree-of-freedom cable-driven upper limb rehabilitation robot and proposed a control algorithm of the passive training for this robot. Comparing with the conventional cable-driven rehabilitation robot, the workspace of this robot is increased by optimizing the distribution of the cable attachment points and by improving the mechanical design. The rotation structure of the upper arm module can change the distribution of the attachment points as needed, by which the cable tension planner can be satisfied in almost all cases. At the meantime, the internal/external rotation of shoulder joint can be achieved without the change of the cables configuration, which is also important for increasing the workspace and comfortability of utilization. The activities of daily living (ADLs) training can be achieved well without any manual adjustment. The related controller for passive training is designed, which includes a higher controller for trajectory tracking and a lower controller for keeping cable tension as the output of the tension planner in real-time. The passive training experiments are conducted on five healthy subjects of different body size. The results demonstrated that the passive training can be achieved well on different subjects and the cable tension controller is also working effectively.
Spectro-Temporal Attention-Based Voice Activity Detection

Voice Activity Detection (VAD) systems suffer from unexpected and non-stationary background noises at magnitudes sufficiently high to mask the speech signal.Although several methods of increasing the performance of VAD have been proposed, their approaches have yet to mitigate the influence of the background noise itself. This letter proposes an effective noise-robust VAD system approach. The proposed method uses spectral attention and temporal attention through applying a deep learning-based attention mechanism. The proposed method is demonstrated and compared with several other deep learning-based methods in terms of the area under the curve in experiments with either known or unknown noise-added, and real-world noisy data. The results show that the proposed method outperforms the other methods in all the scenarios considered, but moreover generalizes well in environments of unknown or unexpected noise.
Deep Learning-Based Mutual Detection and Collaborative Localization for Mobile Robot Fleets Using Solely 2D LIDAR Sensors

Localization for mobile robots in dynamic, large-scale environments is a challenging task, especially when relying solely on odometry and 2D LIDAR data. When operating in fleets, mutual detection and the exchange of localization information can be highly valuable. Detecting and classifying different robot types in a heterogeneous fleet, however, is nontrivial with 2D LIDAR data due to the sparse observation information.In this paper a novel approach for mutual robot detection, classification and relative pose estimation based on a combination of convolutional and ConvLSTM layers is presented in order to solve this issue. The algorithm learns an end-to-end classification and pose estimation of robot shapes using 2D LIDAR information transformed into a grid-map. Subsequently a mixture model representing the probability distribution of the pose measurement for each robot type is extracted out of the heatmap output of the network. The output is then used in a cloud-based collaborative localization system in order to increase the localization of the individual robots.The effectiveness of our approach is demonstrated in both, simulation and real-world experiments. The results of our evaluation show that the classification network is able to achieve a precision of 90% on real-world data with an average position estimation error of 14 cm. Moreover, the collaborative localization system is able to increase the localization accuracy of a robot equipped with low-cost sensors by 63%.
REDTag: A Predictive Maintenance Framework for Parcel Delivery Services

The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a real-world dataset including event-related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e., broken or safe), we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event-related features. To the best of the authors’ knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real-world data.
Study of flexible monopole antenna with coplanar feeding structure fabricated by magnetron sputtering and laser ablation technologies

In this paper, we deal with the study of flexible antennas for ISM range fabricated by our original approach based on magnetron sputtering and laser ablation. We used polyimide film with a thickness of 100 |xm as a flexible dielectric substrate. As a conductive layer we used oxygen free copper and nickel. We used a known sketch for the antenna with a coplanar feeding structure that has already numerically verified, fabricated by the inkjet printing technology and experimentally studied. We have fabricated several flexible antenna samples with coplanar feeding structure using our original approach. Results of scanning electron microscopy and optical microscopy studies of fabricated flexible antennas were considered. Antennas measurements were carried out with the help of an experimental setup based on the vector network analyzer. The experimental measurements were preceded by 1-port type calibration procedure in 0.3-3 GHz band on coaxial ends of cables using automated electronic calibration module. A comparison of obtained experimental results with the already known results from several scientific papers was considered. Fabricated antennas demonstrate reflection loss not worse than 16 dB throughout full ISM band (2.4-2.5 GHz). Obtained experimental results are in good agreement with the numerical ones and other published experimental studies.
Improving Whole-Heart CT Image Segmentation by Attention Mechanism

Decent whole-heart segmentation from computed tomography (CT) can greatly contribute to the diagnosis and treatment of cardiovascular diseases. However, due to the difficulties such as blurred boundaries between neighbouring tissues and a large number of background voxels in medical images, automated whole-heart segmentation is still a challenging task. In this paper, we proposed three modified attention models, including simple negative example mining (SNEM), attention gate (AG) and U-CliqueNet (UCNet), to lead the deep learning network to focus on more salient information. These three attention modules were further implemented into a deeply-supervised 3D UNET separately and jointly, showing different degrees of improvement on the whole-heart segmentation task. Our experiments advised that SNEM was the most simple and effective attention mechanism for medical image processing among the three and the UCNet could reach the best performance. The combination of the attention mechanisms cannot always synergistically increase the accuracy, but joint models would have a positive influence in most cases. Finally, our network achieved a Dice score of 0.9112, which was a substantially higher performance than most of the state-of-the-art methods.
Deep Neural Hybrid Beamforming for Multi-User mmWave Massive MIMO System

Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multiple-input and multiple-output (MIMO) system. However, the design of digital and analog beamformer is a challenge task with non-convex optimization, especially for the multi-user scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.
How the Formal Description of the Transmembrane Proteins can Reduce the Bio-Medical Experiments

A transmembrane transport protein is described in membrane computing, a model of computation inspired by the structure and the functioning of living cells. More exactly, the sodium-potassium exchange pump is described by using membrane systems, and then implemented by using a complex software rewriting engine called Maude. This implementation allows to simulate the pump and then to verify several properties, and so reducing the number of bio-medical experiments.
High-Performance Implementation of Dynamically Configurable Load Balancing Engine on FPGA

Load balancing technology plays an important role in task distribution. At present, mainstream solutions are implemented by software, and the delay introduced is longer; others implemented by hardware do not achieve excellent performance. This article proposes an implementation scheme of a reconfigurable load balancing engine on an FPGA. This engine could distribute network packets to CPU cores over a five-tuple, and the delay brought by balancing algorithm is only about 26 ns. Furthermore, this engine can process all frame sizes of packets at 100 percent line rate and zero packet loss rate, achieving excellent performance. At the same time, this engine supports dynamic configuration and can be set flexibly according to different scenarios.
A Variable Stiffness Elbow Joint for Upper Limb Prosthesis

One of the main research trends toward next-generation prostheses and bionic aids is to better replicate human motor behaviours and to improve the interconnection with the human sensory-motor architecture. One of the natural characteristics of the human arm of utmost importance in our interaction with the environment is our ability to vary the mechanical impedance of our joints by commanding the co-contraction of antagonist muscles. Integration in prostheses of such features is currently under studies. The introduction of physical variable impedance in the mechatronic structure of the devices could at the same time improve interaction and robustness and allow for more sophisticated controls with the goal of naturalness of motion. The system proposed in this paper is a variable stiffness elbow joint for upper limb prostheses that reproduces mechanical abilities of the human joint, in terms of performance, inherent compliance and natural behaviour. This variable stiffness mechanism can be actively controlled by the user, and by using an agonist-antagonistic configuration of proper elastic elements, its output functions are similar to the models of the human muscle. The design and mechanical implementation of the device are detailed in this document together with its experimental validation and characterisation.
Inpainting of Medical Images using a Novel Fourthorder PDE-based Interpolation Technique

A novel inpainting approach that can be applied successfully in the medical imaging domain is proposed in this research paper. It is based on a novel nonlinear fourth-order partial differential equation (PDE)-based model that is then discretized by using an iterative finite difference-based numerical approximation algorithm. The proposed numerical scheme is successfully applied to many images affected by missing regions. Some of these regions, representing wounds, scratches or skin wrinkles in medical images, are inpainted properly by this interpolation technique.
Improved Learning Accuracy for Learning Stable Control from Human Demonstrations*

Learning from Demonstration (LfD) has been identified as an effective method for making robots adapt to a similar kind of tasks. In this work, a framework of learning from demonstration has been proposed for modelling robot motions. We present an approach based on dimension ascending to learn a dynamical system, so that the reproduced motions can closely follow the demonstrations. In addition, the reproductions can ultimately reach and stop at the target, which reflects the robustness of the method. Therefore, the system accuracy and stability can be better guaranteed simultaneously. The effectiveness of the proposed approach is verified by performing handwriting experiments on the LASA data set.
Multiband Circular Polarization Antenna for Satellite System Argos-3 and GPS

A model of combined circular polarization patch antenna for the satellite communication system Argos-3 and GPS was developed and investigated. The antenna consists of three square-shaped emitters, which are located one above the other, and has right-hand circular polarization radiation in three frequency ranges. To improve the matching of the antenna at the upper range, series-tuned circuit between the screen and the middle emitter was added to the antenna.
Modeling and Identification for the Design of a Rotary Soft Actuator based on Wren Mechanism

In this paper, we describe the use of calibration techniques to help improving prediction accuracy of models for soft actuators. A finite-difference model considering anisotropy of soft actuator flexible chamber is combined with experimental identification to assess actuator motion. Identifiability conditions are exploited to determine suitable experimental conditions, taking into account the manufacturing process capabilities, multimaterial additive manufacturing in this context. As a second contribution, a soft actuator with Wren mechanism for reinforcement is being developed. It is considered for rotary actuation, with the description of a proof of concept. The interest of the calibration technique and the actuator design are finally discussed.
GOP Level Quality Dependency Based Frame Level Rate Control Algorithm

This paper proposes a rate control algorithm based on group of pictures (GOP) level quality dependency for high efficiency video coding (HEVC) low delay hierarchical coding structure. Firstly, this paper builds a GOP level quality dependency model. Secondly, a GOP level quality dependency model based GOP level rate-distortion optimization is introduced to make bit allocation more reasonable. Experimental results show that, compared with HM16.7 frame level rate control algorithm, proposed rate control algorithm shows 5.80% and 5.07% BD-rate saving on average under low delay B and low delay P configurations.
Minimizing Latency for 5G Multimedia and V2X Applications using Mobile Edge Computing

The densification of 5G deployments and Internet of Things (IoTs) are envisioned to generate data beyond the handling capacity of currently deployed networks. Moreover, the computations and storage on remote cloud servers which are typically situated far away from the end user bring in additional challenges of increased network latency and bottlenecked network bandwidths. Mobile Edge Computing (MEC) is an emerging technology that provides cloud and IT services including computation, networking and storage closer to the mobile subscribers. Therefore, the key architectural change in 5G is of integrating MECs with the the local cellular base stations which can lower latency and improve throughput. This paper explores the feasibility of co-locating MEC with the base station, and evaluate the performance of two sample use-cases of multimedia applications and Vehicle-to-Vehicle/Infrastructure (V2X) using the commercial OPNET simulator. The results thus obtained demonstrate the adoption feasibility in the use-cases presented.
Adaptive Unscented Kalman Filter-based Disturbance Rejection With Application to High Precision Hydraulic Robotic Control

This paper presents a novel nonlinear disturbance rejection approach for high precision model-based control of hydraulic robots. While most disturbance rejection approaches make use of observers, we propose a novel adaptive Unscented Kalman Filter to estimate the disturbances in an unbiased minimum-variance sense. The filter is made adaptive such that there is no need to tune the covariance matrix for the disturbance estimation. Furthermore, whereas most model-based control approaches require the linearization of the system dynamics, our method is nonlinear which means that no linearization is required. Through extensive simulations as well as real hardware experiments, we demonstrate that our proposed approach can achieve high precision tracking and can be readily applied to most robotic systems even in the presence of uncertainties and external disturbances. The proposed approach is also compared to existing approaches which demonstrates its superior tracking performance.
Audio-visual sensing from a quadcopter: dataset and baselines for source localization and sound enhancement

We present an audio-visual dataset recorded outdoors from a quadcopter and discuss baseline results for multiple applications. The dataset includes a scenario for source localization and sound enhancement with up to two static sources, and a scenario for source localization and tracking with a moving sound source. These sensing tasks are made challenging by the strong and time-varying ego-noise generated by the rotating motors and propellers. The dataset was collected using a small circular array with 8 microphones and a camera mounted on the quadcopter. The camera view was used to facilitate the annotation of the sound-source positions and can also be used for multi-modal sensing tasks. We discuss the audio-visual calibration procedure that is needed to generate the annotation for the dataset, which we make available to the research community1.1http://cis.eecs. qmul.ac. uk/projects/avq/
An Augmented Method of Selecting Fashion Talent by Adding Social Media Characteristic

Fashion model plays an important role in presenting designer’s work by showing how the cut of fabric interplayed with the body. In selecting the fashion models, the agency considers physical characteristics that express the aesthetic. Another subjective advantage of fashion model is the appearance on professional network. However, the emerging of social media culture has revolutionized fashion industry in producing and consuming fashion. In term of fashion modelling, social media open the opportunities for talent to "breaking in" and "getting discovered". Prior research has dealt with predicting success based on social media presence. Thus, in this paper we construct additional social media activity to predict a fashion model success. We examine prediction using classification task by utilizing Random Forest and Support Vector Machine. Our research finds that social media activity improves the accuracy by 4.55% increasing up to 84.55% performed by Random Forest.
The effect of fetal growth restriction and sex on the development and function of adipose tissue.

XIII LIST OF TABLES AND FIGURES . XVI COMMONLY USED ABBREVIATIONS . XIX 1. LITERATURE REVIEW . 1 1.1 FETAL GROWTH RESTRICTION AND LOW BIRTH WEIGHT . 1 1.2 THE FETAL ORIGINS OF OBESITY AND INSULIN RESISTANCE . 3 1.2. 1 The Association Between Low Birth Weight, Visceral Obesity and the Development of Insulin Resistance . 3 1.2. 2 The Association Between Low Birth Weight, Subcutaneous Adiposity and the Development of Insulin Resistance . 6 1.3 INSULIN-LIKE GROWTH FACTORS AND FETAL GROWTH . 10 1.4 INSULIN-LIKE GROWTH FACTORS AND ADIPOSE TISSUE DEVELOPMENT . 14 1.5 PPARγ, OBESITY AND INSULIN SENSITIVITY . 17 1.6 LEPTIN AND THE REGULATION OF FAT MASS . 20 1.7 ADIPONECTIN AND INSULIN SENSITIVITY . 26 1.8 THE ROLES OF LPL, G3PDH AND GAPDH IN ADIPOCYTE METABOLISM . 29 1.8. 1 Lipoprotein Lipase (LPL) . 30 1.8. 2 Glycerol-3-Phosphate Dehydrogenase (G3PDH) . 31 1.8. 3 Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) . 32 1.9 THE EXPERIMENTAL INDUCTION OF IUGR IN ANIMAL MODELS . 34 1.10 IUGR AND THE DEVELOPMENT OF POSTNATAL ADIPOSITY IN ANIMAL MODELS . 37 1.11 IUGR AND THE DEVELOPMENT OF POSTNATAL INSULIN RESISTANCE IN ANIMAL MODELS . 40
Analysis of Template-Based Detection Algorithms for Inshore Bryde’s Whale Short Pulse Calls

Marine mammals use sound for communication and echolocation within their ecosystems. The detection of these sounds is an important aspect of signal processing, such that we can estimate the spatial position and direction of arrival of these mammals, and have an understanding of their ecology. Passive acoustic monitoring (PAM) is widely used to understand marine mammal movement and vocal repertoire. In PAM, datasets are accumulated over days, months or years. Thus, it is impracticable to manually analyse the datasets because it is very large. This motivated the development of automated sound detection techniques for marine mammals, which most often varies depending on the vocal duration, frequency range and call type. In this paper, continuous recordings of Bryde’s whale (Balaenoptera edeni edeni) short pulse calls ( $ < 3.1s$ long) were collected on a weekly basis from December 2018 to April 2019 on sighting of the individual in a single site in the endmost South-West of South Africa. The sound, previously not documented off South Africa, was observed on visual confirmation of the presence of inshore Brydes’s whale. In addition, the paper develops and analyses two automated template-based detection algorithms for this short pulse call, employing dynamic time warping (DTW) and linear predictive coding (LPC) techniques. These proposed template-based detectors are novel, as they have not being previously used in Bryde’s whale sound detection in the literature. When applied to the continuous recordings of the short pulse calls, the DTW-based and LPC-based detection algorithms obtained a sensitivity of 96.04% and 97.14% respectively for high signal-to-noise ratio (about $10dB$ above the ambient sound). Otherwise, for low SNR, the DTW-based and LPC-based detection algorithms obtained a sensitivity of 94.98% and 96.00% respectively. These detection algorithms exhibit low computational time complexity and can be modified to analyse the movement of obscure but vocal marine species instead of manual identification.