Miracle-dz/UniFAR-FTUs huggingface dataset:
Affordable, Multi-Function Flight-Worthy Airborne Phased-Array Sensor

A dual-beam, dual-polarized wide-beam scanning phased array is designed, fabricated, assembled and tested at Ku-band for airborne sensing and communication applications. Compared with a prior proof-of-concept COTS-based phased-array, this demonstration uses custom MMICs to double the number of beams, double the transmit power output, increase the grating-lobe free beam scanning range from 50° to 60° and add additional array functionality. Custom MMICs and ASICs add more phase- and amplitude-bits and more than 20 dB in amplification significantly stressing transition matching, grounding and cavity mode suppression requirements. A custom seal ring over the entire array helps to choke-out cavity modes and increases isolation to ensure electronic stability across the aperture. The metallic seal ring also removes heat away from the electronics. A 120° dual-beam scanning volume on receive and switchable beams on transmit has been demonstrated and used on a wide range of sensor and communication applications.
Power Splitting-Based SWIPT Systems With Full-Duplex Jamming

The simultaneous wireless information and power transfer (SWIPT) has recently attracted much attention since both information and energy are integrated within radio frequency signals, which is considered to be one of the most important technologies for future networks. However, security becomes one of the most critical issues in SWIPT networks due to its broadcasting features over wireless media and the transferring requirements for strong signal strength. To improve the security of SWIPT networks, in this paper, a SWIPT system enabled by full-duplex (FD) jamming is proposed, where an FD energy-limited receiver with the power splitting (PS) structure, powered by a transmitter, generates jamming signals. Both linear and nonlinear energy harvesting (EH) models are considered. To evaluate the secrecy throughput (ST) of the system, we derive the closed-form connection outage probability and secrecy outage probability for the proposed PS-based scheme in the delay-constrained transmission (DCT) mode. The ST is maximized by optimizing the PS ratio based on the asymptotic cases of the high signal-to-noise ratio as well as the perfect self-interference cancellation (SIC). Besides, the exact integral forms of the ergodic secrecy capacity and their closed-form lower bounds are presented for both the PS-based and the time switching (TS)-based schemes in the delay-tolerant transmission (DTT) mode. Simulation results suggest that the PS-based scheme outperforms the TS-based scheme for the DTT mode. For the DCT mode, the PS-based scheme can achieve better performance than the TS-based scheme only in low target communication rate, small PS/TS ratio, high transmit power, and weak path loss. Meanwhile, given a tolerable amount of SIC, the ST obtained in the linear EH model is higher than that achieved in the nonlinear EH model.
Learning prohibited and authorised grasping locations from a few demonstrations

Our motivation is to ease robots’ reconfiguration for pick and place tasks in an industrial context. This paper proposes a fast learner neural network model trained from one or a few demonstrations in less than 5 minutes, able to efficiently predict grasping locations on a specific object. The proposed methodology is easy to apply in an industrial context as it is exclusively based on the operator’s demonstrations and does not require a CAD model, existing database or simulator. As predictions of a neural network can be erroneous especially when trained with very few data, we propose to indicate both authorised and prohibited locations for safety reasons. It allows us to handle fragile objects or to perform task-oriented grasping. Our model learns the semantic representation of objects (prohibited/authorised) thanks to a simplified data representation, a simplified neural network architecture and an adequate training framework. We trained specific networks for different objects and conducted experiments on a real 7-DOF robot which showed good performances (70 to 100% depending on the object), using only one demonstration. The proposed model is able to generalise well as performances remain good even when grasping several similar objects with the same network trained on one of them.
Pulse Profiling Active Load Pull Measurements

An advanced active load-pull pulse profiling (ALPPP) system is presented to identify and track, for the first time, the optimum impedances along different sections of an RF pulse. Optimum impedance variations of a 10W GaN device within the RF pulse, and the resulting performance variations are quantified over an output power range of 25 dB showing a significant variation at power back-off. Furthermore, the nature of optimum load variations is probed through the addition of a pulsed IV capability. Pre-bias is utilized to investigate the impact of traps on device behavior over a range of temperature and drive power levels. It is demonstrated that pre-filling the traps provides for an almost constant load optimum across the RF pulse while maintaining device performance. Moreover, the identified optimum pre-bias condition, shows improved device linearity over the duration of the RF pulse, hence, suggesting a new method for compensating impedance mismatches and improving device linearity within an RF pulse.
Performing Human-Robot Interaction User Studies in Virtual Reality*

This study investigated whether virtual reality could be used as platform for conducting human-robot interaction user studies. It was investigated whether user studies performed in virtual reality elicited realistic responses from participants. To answer this question, a real world study was replicated as closely as possible in virtual reality, where a robot tour guide asked participants to keep a secret. The experiment consisted of a virtual museum tour where the robot acted as the tour guide while displaying either social or non-social behaviour.The measurements taken in this study were the objective measurement whether the participants kept the robot’s secret or not. Questionnaires were taken to investigate participants’ perception of the robot and its feelings, as well as their experienced level of presence and their tendency to become immersed in the virtual environment. Results show that the participants responded differently in the virtual reality study when compared to the original real world study, where the secret was kept more often for the non-social robot, but less often for the social robot. In both the original and replicated study a strong, positive correlation was found between participants’ perception of the robot as a social being and their tendency to keep the robot’s secret.These inconclusive findings, some changes that were required for the virtual environment compared to the original study, and different participant demographics indicate that more work is needed to determine whether virtual reality can be used as a tool to conduct human-robot interaction experiments.
Design and implementation of a cloud-based multi-LPWAN IoT data collection service

With the apparent ever-growing trend of Internet- of-Things (IoT) devices, there is a similar demand to keep control over the data generated. This means that the companies behind integrated IoT services need to be in charge of the whole process, from the data generation to the collection. In this paper, we focus on the design and implementation of a data collection service for multiple low-power wide-area network (LPWAN) technologies. Therefore, we present the ongoing work of a multi-LPWAN IoT data collection service that explores the capacity needed to collect and serve as a single IoT data collector/provider service. This service can concurrently collect data generated from multiple IoT wireless modules and offer it via a single, unified and secure REST API. The implemented service tries to tackle the burden of dealing with multiple LPWAN carriers while keeping a coherent backend system that can securely integrate and deliver data.
A Capsule Network-based for identification of Glaucoma in retinal images

Glaucoma is an eye disease responsible for the second most common cause of blindness in the world. The need to detect this disease in its early stages is notorious, considering that late treatment can cause loss of vision. In this context, computational methods are being developed to assist specialists in the task of analyzing ocular images to provide greater precision to the diagnosis. In this paper, we present a methodology for automatic classification of glaucoma using Capsule Network (CapsNet), a recent model of deep learning that analyzes the hierarchical spatial relationships between characteristics to represent images, so that it requires fewer training samples than traditional CNNs to achieve efficient classification. Before the execution of CapsNet, we applied a pre-processing step to the images, in order to highlight the characteristics. Our results were promising, with 90.90% accuracy, 86.88% recall, 94.64% precision, 90.59% f1-score, 0.904 AUC and 0.801 kappa index. The main contribution of our method is the fact that we have achieved promising results without the need to apply data to increase and segment the region of the optical disc. Thus, our study showed the potential of the capsules in identifying the relationships between the characteristics, even in the face of a reduced set of training.
Hybrid Dynamic Probability-Based Modeling Technique for Rolling Stock Failure Analysis

The purpose of this study is to propose a novel hybrid dynamic probability-based failure analysis technique consisting of dynamic Bayesian discretization (DBD) and stochastic Petri nets (SPNs) for railway rolling stock (RS) failure analysis. Performing failure analysis and diagnoses for integrated RS subsystems is challenging and can lead to operational delays affecting fleet reliability and availability. This paper presents an integrated feature of updative adaptation using DBD methods to analyze prior continuous and discrete probability data—by means of evidence-based propagation to ascertain posterior faulty component states and simultaneously allowing for rapid failure notification, detection, and isolation of multiple RS subsystems using the reachability tree characteristics of SPNs. Unlike other dynamic probability methods, the DBD-SPN hybrid model presented here reduces computational time and enhances convergence accuracy using the Kullback–Leibler measure, sequential event analysis, and stable and low-entropy-error characteristics. In an extensive UK-based RS case study, it was observed that this approach is suitable for rapid failure notification, detection, and isolation of traction door interlock failure. It is also believed that the current study represents a useful contribution to the research and technology of hybrid DBD and SPNs for the failure analysis of a system consisting of multiple subsystems, since its application makes the difference between being able to evaluate realistically common cause and sequential failure analyses of complex systems.
SVM Assisted Primary User-Detection for Non-Cooperative Cognitive Radio Networks

This paper presents a new blind spectrum sensing (SS) algorithm based on a machine learning model: the radial basis function support-vector machines (RBF-SVM). As features, the introduced approach uses statistical tests that are based on the eigenvalues of the received signals covariance matrix. Since the decision on the frequency resource occupancy is in fact an issue of labeling binary data, SVM is intended as a potential technique for SS paradigm. The flexibility of SVM for linearly non-separable and high dimensional data makes it a good candidate for our issue, particularly that we consider low signal to noise ratios (SNR). Computer simulations shows that the proposal outperforms classical non-cooperative SS algorithms.
When Positive Perception of the Robot Has No Effect on Learning

Humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants’ learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for schoolchildren, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot’s behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants’ engagement with the activity (Intrinsic Motivation Inventory—IMI), their mutual understanding (IMIlike) and perception of the robot (Godspeed Questionnaire); (ii) participants’ performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams’ performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.
Selection of Antagonistic Strains Against Watermelon Wilt and Their Biocontrol Efficiency

antagonistic Trichoderma spp. and 10 bacterial isolates against the pathogenic Fusarium oxysporum f. sp. niveum were isolated and selected from hundreds of soil and plant roots samples. Among the strains, Trichoderma viride TR 8 and Bacillus sp. B67 were the most effective ones and fermented to prepare their inocula and their mixture named Jiangenbao. Inoculation tests in greenhouse showed that all the biocontrol agents had good control effectiveness for control of watermelon wilt disease. Jiangenbao dust was more effective than its rwo constituents used alone. In field trials, when Jiangenbao dust diluted with soil at the ratio of 1∶50 was applied into seed delves before sowing, 80% control effect against watermelon wilt was obtained, which was apparently higher than that of carbendazim and amino oligosaccharin. Furthermore, it was also observed that Jiangenbao dust could enhance watermelon plant growth.
The Influence of Gestational Diabetes Mellitus upon the Selected Parameters of the Maternal and Fetal System of Insulin-Like Growth Factors (IGF-1, IGF-2, IGFBP1-3)—A Review and a Clinical Study

Background: Gestational diabetes mellitus (GDM), defined as impaired glucose tolerance with onset or first recognition in pregnancy, increases the risk of not only maternal but also fetal and neonatal complications. Given the structural similarity of insulin-like growth factors with insulin and participation of components of the insulin-like growth factor system in glucose homeostasis, we hypothesized that the IGF axis is involved in the development of GDM complications or its pathogenesis. The aim of this study was to evaluate the effect of GDM on the selected parameters of the insulin-like growth factors (IGF-1, IGF-2, IGFBP1-3) in the maternal and fetal blood. Methods: The clinical material of this case-control study included 109 pregnant women and their offspring. The study group (n = 120) consisted of 60 patients with diagnosed gestational diabetes and their newborn babies. The control group (n = 98) comprised 49 healthy parturients and their offspring. We measured the concentrations of IGF-1, IGF-2, IGFBP-1, IGFBP-2, IGFBP-3, insulin and glucose made by the ELISA method in peripheral blood serum in patients suffering from GDM and pregnant women without GDM, and in the umbilical cord blood of newborn babies born to them. Results: The analysis of concentrations of IGF-1, -2 and IGFBP-3 in peripheral blood as well as umbilical cord blood did not demonstrate a statistically significant difference between the study group and the control group. Significantly lower concentration of IGFBP-1, IGFBP-2 in peripheral blood and in umbilical cord blood was detected in the study group in comparison to the control group. A statistically positive correlation between the concentration of IGF-1 in umbilical cord serum of newborn babies born to women with gestational diabetes and the length of a baby after its birth was observed. Conclusions: Gestational diabetes mellitus does not significantly affect the concentrations of IGF-1, -2, IGFBP-3 in the peripheral blood and umbilical cord blood, but has the greatest influence on maternal and fetal IGFBP-2 concentrations. A positive correlation between the concentration of IGF-1 in umbilical cord blood and the length of a newborn suggests an influence of IGF-1 on the process of fetal development.
A Dyadic Perspective of Felt Security: Does Partners’ Security Buffer the Effects of Actors’ Insecurity on Daily Commitment?

Interdependence and attachment models have identified felt security as a critical foundation for commitment by orientating individuals towards relationship-promotion rather than self-protection. However, partners’ security also signals the relative safety to commit to relationships. The current investigation adopted a dyadic perspective to examine whether partners’ security acts as a strong link by buffering the negative effects of actors’ insecurity on daily commitment. Across two daily diary studies (Study 1, N = 78 dyads and Study 2, N = 73 dyads), actors’ X partners’ daily felt security interactions revealed a strong-link pattern: lower actors’ felt security on a given day predicted lower daily commitment, but these reductions were mitigated when partners reported higher levels of felt security that day. Actors’ X partners’ trait insecurity (attachment anxiety) interaction also showed this strong-link pattern in Study 1 but not Study 2. The results suggest that partners’ felt security can help individuals experiencing insecurity overcome their self-protective impulses and feel safe enough to commit to their relationship on a daily basis.
Identification of factors related to fertilization failure in in vitro fertilization-embryo transfer.

OBJECTIVES To investigate the possible factors relevant to fertilization failure in in vitro fertilization-embryo transfer (IVF-ET). METHODS The medical records of 4 205 infertile patients undergoing IVF-ET treatment at the Reproductive Medicine Center, Xiangya Hospital, Central South University from January 2016 to December 2017 were collected. The patients were divided into a complete fertilization failure group, a low fertilization rate group, and a control group based on fertilization rate. We examined the associations among the 3 groups in terms of female age, duration of infertility, duration of stimulation, gonadotropin (Gn) dosage, follicle-stimulating hormone (FSH) dosage, and total number of retrieved oocytes. According to theincidence factors, the patients were divided into a single female factor group, a single male factor group and a unisex factor group, and the correlation analysis of incidence factor among the 3 groups was performed. The patients were divided into a primary infertility and a secondary infertility in accordance with the type of infertility. We analyzed the correlation of infertility type among the three groups. Risk factors for complete fertilization failure and low fertilization rate in IVF-ET were obtained by stepwise multiple linear regression analysis. RESULTS Primary infertility, long infertility duration, total number of retrieved oocytes, and unisex factor were associated with completefertilization failure and low fertilization rate in IVF-ET (P<0.05), but female age, duration of stimulation, FSH dosage as well as Gn dosage were not correlated with complete fertilization failure and low fertilization rate in IVF-ET (P>0.05). Stepwise multiple linear regression analysis showed that the incidence factor, type of infertility, and infertility duration were independent influential factors for complete fertilization failure and low fertilization rate. CONCLUSIONS Complete fertilization failure and low fertilization rate in IVF-ET are related to duration of infertility, total number of retrieved oocytes, cause of onset, and type of infertility, but they are not relevant to female age, duration of stimulation, and Gn and FSH dosage.
Closed-Loop Sign Algorithms for Low-Complexity Digital Predistortion

In this paper, we study digital predistortion (DPD) based linearization with specific focus on millimeter wave (mmW) active antenna arrays. Due to the very large channel bandwidths and beam-dependence of nonlinear distortion in such systems, we propose a closed-loop DPD learning architecture, look-up table (LUT) based memory DPD models, and low-complexity sign-based estimation algorithms, such that even continuous DPD learning could be technically feasible. To this end, three different learning algorithms - sign, signed regressor, and sign-sign - are formulated for the LUT-based DPD models, such that the potential rank deficiencies, experienced in earlier methods, are avoided. Then, extensive RF measurements utilizing a state-of-the-art mmW active antenna array system at 28 GHz are carried out and reported to validate the methods. Additionally, the processing and learning complexities of the considered methods are analyzed, which together with the measured linearization performance figures allow to assess the complexity-performance tradeoffs. Overall, the results show that efficient mmW array linearization can be obtained through the proposed methods.
Sexual function outcomes and risk factors of erectile dysfunction after surgical repair of penile fracture.

OBJECTIVE To determine the erectile dysfunction (ED), overall sexual function, and risk factors for developing ED after surgical repair of penile fracture. MATERIAL AND METHODS This was an ambispective observational study conducted from September 2014 to August 2019, which included 68 patients with a clinical diagnosis of penile fracture. The clinical presentation, etiology, and surgical details were recorded. Patients were contacted via telephone and called for follow-up. Their sexual function was objectively recorded using the sexual health inventory for men questionnaire, erection hardness grading scale, and the brief male sexual function inventory (BMSFI). Patients were categorized in 2 groups on the basis of ED. These 2 groups were compared on the basis of preoperative and intraoperative factors to determine the predictors of postoperative ED. RESULTS The mean age at presentation was 33.64±9.46 (range, 19-54) years. The most common mode of injury was injury during the sexual intercourse (78%). All the patients underwent surgical exploration through subcoronal degloving incision. On follow-up, 7 patients (11.3%) developed ED (mild ED, 5 patients; mild-to-moderate ED, 2 patients). Per BMSFI, 58 (93.5%) patients had no bothersome issues with their sexual life, and they were mostly satisfied. The significant risk factors for ED were age >50 years and bilateral corporal involvement. CONCLUSION Penile fracture is a true urological emergency, and prompt diagnosis by clinical acumen and emergent surgical exploration provide good functional results with low morbidity. The main predictors of postsurgical ED are increasing age (>50 years) and bilateral corporal involvement.