Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Transmission LiFi

Light fidelity (LiFi) is a relatively new wireless communication technology that exploits the optical spectrum. Compared to wireless fidelity (WiFi), LiFi is densely deployed with each access point (AP) covering an area only a few meters in diameter. Also, LiFi users are susceptible to intermittent light-path blockages. Meanwhile, the user is served by a single AP in conventional LiFi systems. This would cause frequent handovers for LiFi users, resulting in a degradation in quality of service. In this paper, parallel transmission is investigated for LiFi, which is named PT-LiFi. With a delicate design of the transmitter and the receiver, PT-LiFi enables multiple LiFi APs to serve the user simultaneously. Particularly, data transmission continues without interruption when the user is losing connectivity to some of the connected APs. Resource allocation is studied for the PT-LiFi system, and a novel load balancing method is proposed to jointly allocate resource across the APs. Results show PT-LiFi can make efficient use of the densely deployed LiFi APs and provide a flexible way of load balancing. Compared with a conventional LiFi system, the proposed method can increase user throughput by up to 150% and improve user fairness by up to 15%.
Active Learning of Signal Temporal Logic Specifications

In this paper, we propose a method to infer temporal logic behaviour models of an a priori unknown system. We use the formalism of Signal Temporal Logic (STL), which can express various robot motion planning and control specifications, including spatial preferences. In our setting, data is collected through a series of queries the learning algorithm poses to the system under test. This active learning approach incrementally builds a hypothesis solution which, over time, converges to the actual behaviour of the system. Active learning presents several benefits compared to supervised learning: in the case of costly prior labelling of data, and if the system to test is accessible, the learning algorithm can interact with the system to refine its guess of the specification of the system. Inspired by mobile robot navigation tasks, we present experimental case studies to ensure the relevance of our method.
Development of features of the autonomic circulatory regulation in late premature and full term infants

Objective: In this work, the development of the features of an autonomic system of blood circulatory regulation from the period of late prematurity to full-term infants is investigated using the example of signals of the photoplethysmogram of blood vessels, electrocardiogram, and respiration. The previous works of foreign and domestic authors show that interdependency between physiological rhythms emerges early in development. Much attention was paid to the study of cardiorespiratory coupling and his development in newborns, nevertheless, there are still many questions in this area. In this study, in addition to studying the standard low-frequency cardiorespiratory effect, we studied the relationships between the low-frequency components of the autonomic regulation of heart rate (using an electrocardiogram) and the tone of the arterial vessels (using photoplethysmogram of blood vessels). Approach: The goal of the work is to estimate the dynamics of the development of the coupling of the autonomic regulatory circuits of the cardiovascular system and respiratory system. In this paper, special attention is paid to the study of phase synchronization of these processes, as well as to the interaction of frequency components. We studied the coherence between heart rate variability, photoplethysmograms of blood vessels, and respiration signals of newborns with a gestational age of 30 weeks to 40 weeks.Main results: No significant changes in the measure of the estimation of phase synchronization with increases in the gestational age of the infant were found. The reason may be a notable change in the degree of connectedness of the studied systems after birth. There was also an assumption about the presence of changes in the direction of connectedness, but not in its strength.Significance: This study carries a fundamental interest - an understanding of the higher risk for negative results at the end of a premature population. Moreover, the continuation of work in this direction may allow creating a tool for the development of early markers of impaired autonomic regulation of the functions of the cardiovascular system and respiration in young children.
Cascaded Double Beaulieu-Xie Fading Channels

In this letter, a mathematical characterization for the cascaded double Beaulieu-Xie (BX) fading model is presented. Exact and new expressions are determined for the probability density function (PDF) and for the cumulative distribution function (CDF) of the random variable that models the envelope of the cascaded double BX fading, and for the PDF and the CDF of the instantaneous signal-to-noise ratio received. Expressions are obtained for the moment generating function, higher order moments, average channel capacity, outage capacity, outage probability, amount of fading, channel quality estimation index and symbol error probability, for different modulation schemes, of the channel model considered. Several curves, corroborated by simulations performed with Monte Carlo method, are presented for different parameters that characterize the fading model.
Breaking a fully Balanced ASIC Coprocessor Implementing Complete Addition Formulas on Weierstrass Elliptic Curves

In this paper we report on the results of selected horizontal SCA attacks against two open-source designs that implement hardware accelerators for elliptic curve cryptography. Both designs use the complete addition formula to make the point addition and point doubling operations indistinguishable. One of the designs uses in addition means to randomize the operation sequence as a countermeasure. We used the comparison to the mean and an automated SPA to attack both designs. Despite all these countermeasures, we were able to extract the keys processed with a correctness of 100%.
Towards Lightweight Spindle of CNC Lathe Using Structural Optimization Design for Energy Saving*

The spindle system of computer numerical control (CNC) lathe is the principal energy consumer in the machine tool operating stage. Structural optimization design of spindle is regarded as an effective strategy for energy saving. Compared with previous optimization design methods for improving deformation, vibration and other traditional performance indicators of spindle, this paper proposes a method comprehensively considering energy consumption and traditional performance. Firstly, we bulid an energy consumption function of spindle. Structure parameters that have the great influence on the indicators are selected as decision variables. Then, a comprehensive performance objective is determined. Finally, a hybrid algorithm is developed to solve the optimization model. The results show that the optimized structure can decrease the energy consumption while ensuring the static and dynamic performance.
Design and closed loop control of a 3D printed soft actuator

Soft robots, made of soft materials such as dielectric elastomer or shape memory polymers, have received tremendous attentions due to its dexterousness, flexibility and safety compared with rigid robots. However, wider application of soft robots is limited due to their complex fabrication process and poor controllability. Here, we introduce a closed loop controlled soft actuator that is fully 3D printed with flexible material. The structure of the soft actuator is optimized with Finite Element Method (FEM) to acquire shortest fabrication time and highest deformation for same stimulus input. A desktop Fused Deposition Modeling (FDM) 3D printer is used for low-cost fabrication of such actuators. A webcamera is used for the image feedback which offers the real time shape monitoring of the soft actuator. An output feedback Proportional Integral Derivative (PID) controller with lowpass filter is developed with pole placement design method based on a data-driven model of the 3D printed soft actuator. The controller is implemented to regulate the input air pressure to ensure a fast-response, precise and robust shape changing for any work environments.
Dynamic Channel Allocation Algorithm for Spectrum Sharing between Different Radio Systems

For the efficient use of limited frequency resources, the spectrum sharing allocation between mobile networks and different existing radio systems is promising. In this scheme, we focus on an allocation method that involves sharing spectrum bands with a mobile network operator (MNO). Recently, to improve the spectrum efficiency, dynamic spectrum allocation methods according to the MNO demand were proposed. However, in demand-based dynamic allocation, channel discontinuity in the frequency and time directions in the same MNO occurs when mapping the channel to MNOs. This channel discontinuity reduces the utility of shared frequencies. To address this problem, we propose a novel spectrum allocation method that maximizes frequency and time continuity for demand-based dynamic spectrum sharing. Our performance evaluation shows that compared with the existing methods, the proposed algorithm can improve the spectrum efficiency by 6%, channel continuity by 23%, and fairness by 5%.
Studies on the pollution levels of antibiotic resistance genes in Jiulong River estuary and wastewater treatment plants in Xiamen

[Objective] Recently,as the prevalence of multiple antibiotic resistant bacteria in environments are induced by the extensive use of antibiotics by human,a better understanding of the distribution,transport and dissemination of antibiotic resistance genes(ARGs) in envi-ronments is warranted.The objective of this study was to determine the pollution levels of an-tibiotic resistance genes in Jiulong River estuary and wastewater treatment plants(WWTPs) in Xiamen. [Methods] Polymerase chain reaction(PCR) assays were employed to identify the distribution patterns of four sulfonamide,thirteen tetracycline ARGs and two integron genes in water,sediments of Jiulong River estuary and five activated sludges in WWTPs of Xiamen.Clone libraries of the tet(W) gene were also generated from three sediments of Jiulong River estuary and five WWTPs in Xiamen. [Results] The all ARGs has been dectected except tet(O) and tet(S). The results demonstrated that the frequency of detection(FOD) of ARGs and inte-gron genes in environmental samples with the highest FOD in activated sludges(0.86),mod-erate FOD in sediments(0.57) and the lowest FOD in water(0.24). Furthermore,the FODs of sul(l),int(1),tet(A),tet(C),tet(E),tet(M) and tet(W) were higher in freshwater and brackish water than those in seawater,suggesting that ARGs may be transported from the upstream of Jiulong River. [Conclusion] Principle component analysis also supported the suggestions that WWTPs contained the highest ARGs,and ARGs are enriched in sediments but unstable in wa-ter.In addition,the results of tet(W) libraries indicated that WWTPs may be a point source of ARGs into Jiulong River estuary and Xiamen sea.
Low-Complexity Beam Training for Tracking Spatially Consistent Millimeter Wave Channels

In fifth generation (5G) wireless systems, millimetre wave (mmWave) frequency bands will be exploited to support high data demands. The exploitation of mmWave carrier frequencies allows the use of large-scale antenna arrays for three-directional beamforming. To utilise the beamforming gain, the transmitter and the receiver are required to perform beam training periodically to maintain reliable connectivity. This brings an inevitable training overhead for beam measurements. In this paper, an efficient beam training strategy is proposed and the trade-off between the data throughput and the training overhead is investigated for dynamic mmWave channels including blockage effects. By utilising the sparse nature of mmWave channels and the spatial consistency feature of realistic channels, only a limited number of beams are stored and searched based on the recent history of beam selection results. Simulation results show that the proposed beam training strategy can significantly reduce the training overhead while achieving comparable data throughput to the exhaustive beam search.
Data Augmentation for Deep Learning based Cattle Segmentation in Precision Livestock Farming

Accurate segmentation of cattle is a prerequisite for feature extraction and estimation. Convolutional neural networks (CNN) based approaches that train models on the largescale labeled datasets have achieved high levels of segmentation performance. However, pixel-wise manual labeling of a cattle image is challenging and time consuming due to the irregularity of the cattle contour. In this regard, data augmentation for deep learning based cattle segmentation is required. Our proposed data augmentation approach uses random image cropping and patching to expand the number of training images and their corresponding labels, then, a state-of-the-art deep neural net is trained to segment cattle images. Here we apply these techniques to images of cattle in a feedlot environment. Our data augmentation-based approach segmented cattle from a complex background with 99.5% mean Accuracy (mAcc) and 97.3% mean Intersection of Unions (mIoU), improving current techniques including a combination of random flipping, rotation and color jitter.
Requirements Management in Automotive: an Empirical Study on Process Improvement Areas

In the automotive domain the development of software-intensive components is mainly demanded to specialized suppliers that are required by car manufacturers a.k. a. OEMs (Original Equipment Manufacturer) to measure and, eventually, improve their development process by applying process models such as Automotive SPICE. Automotive SPICE is therefore a widely-applied reference framework providing a set of requested practices in the development life-cycle, including requirements management. Requirements management is a key issue in automotive because the high volatility of requirements during projects and the need of interactions among different stakeholders. This paper aims at contributing in identifying what are the most frequent weaknesses in requirements management in automotive. The authors present the results of an empirical study aimed at characterizing and analyzing recurrent requirement management weaknesses in automotive. The authors, as Automotive SPICE assessors, have evaluated requirement management practices on the basis of the evidences gathered from real industrial development projects during a significant number of assessments performed at several organizations world-wide. This paper is intended to derive a picture of the state-of-the-practice of requirements management in automotive focusing on the development of software-intensive automotive components. The purpose is to provide researchers and practitioners with a reference for improvement initiatives aimed at solving those weaknesses.
Enabling Technologies for Smart Construction Engineering: a Review

Data-driven knowledge extraction is becoming one the greatest source of information to study and manage complex systems. Today, new challenges can be provided thanks to Information and Communication Technologies and IoT paradigm that are allowing big mass of data to be stored, processed and analyzed. In this paper an insight on data driven knowledge extraction potentiality in the field of smart construction engineering is given. In particular, the main technologies for supporting the decision-management phase in Architecture, Engineering and Construction Industry (AEC Industry) are analysed. To this aim, such technologies are classified in four fields and the main contributions presented in the related literature are singled out and discussed. Finally, some examples of the current applications in the AEC industry are presented and the gap for future development in this field is enlightened.
Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification

This article proposes a novel unsupervised domain adaptation (DA) method called ideal regularized discriminative multiple kernel subspace alignment (IRDMKSA) for hyperspectral image (HSI) classification. The proposed IRDMKSA method includes three main steps: ideal regularization, discriminative multiple kernel learning, and subspace alignment. The ideal regularization strategy exploits label information of source domain to refine the standard source and target kernels and also to build a connection between them. The discriminative multiple kernel learning can learn a composite kernel to describe the nonlinearity of HSI samples by fusing complementary information among different single kernels. Finally, the subspace alignment is used to diminish the difference between source and target composite kernels. The proposed IRDMKSA method exploits both the sample similarity and label similarity and makes the resulting kernel more appropriate for DA tasks. Experimental results on four DA tasks show that the performance of IRDMKSA is better than some classical unsupervised DA methods for the HSI classification.
A 5-6 GHz Low-Noise Amplifier with > 65-dB Variable-Gain Control in 22nm FinFET CMOS Technology

This paper presents a 5-6 GHz fully-differential low-noise amplifier in 22-nm FinFET Low-Power CMOS technology. To achieve wide gain control, the three-stage LNA consists of a Tee-type RF attenuator and a current-steering variable-gain amplifier. Three-winding transformers are implemented for input and inter-stage matching to achieve simultaneous noise and power matching. The three-stage LNA provides 30.5-dB gain, 1.9-dB minimum noise figure, -29.2-dBm IP1dB and -19.9-dBm IIP3 at 5.7 GHz, and > 65-dB gain-control range with the total DC power consumption of 39 mW at 1-V supply voltage.
Reactive grasping using high-resolution tactile sensors

This paper demonstrates the effectiveness of using high-resolution tactile sensors for a two-finger gripper for reactive grasping under conditions featuring uncertainty, whereas uncertainty will be understood as positioning inaccuracy induced by grasping planners and vision systems. Reactive grasping is defined here as the adoption of gripper pose in accordance to measured tactile sensor feedback. We propose a reactive grasping algorithm that in combination with high-resolution tactile sensors achieves grasping of unknown and novel objects with only one correction move. Extensive tests of the algorithm in different configurations showed a success rate of 91 % compared to 31 % without it.
Line-Based 2-D–3-D Registration and Camera Localization in Structured Environments

Accurate registration of 2-D imagery with point clouds is a key technology for image-Light Detection and Ranging (LiDAR) point cloud fusion, camera to laser scanner calibration, and camera localization. Despite continuous improvements, automatic registration of 2-D and 3-D data without using additional textured information still faces great challenges. In this article, we propose a new 2-D–3-D registration method to estimate 2-D–3-D line feature correspondences and the camera pose in untextured point clouds of structured environments. Specifically, we first use geometric constraints between vanishing points and 3-D parallel lines to compute all feasible camera rotations. Then, we utilize a hypothesis testing strategy to estimate the 2-D–3-D line correspondences and the translation vector. By checking the consistency with computed correspondences, the best rotation matrix can be found. Finally, the camera pose is further refined using nonlinear optimization with all the 2-D–3-D line correspondences. The experimental results demonstrate the effectiveness of the proposed method on the synthetic and real data set (outdoors and indoors) with repeated structures and rapid depth changes.
Good Neighbor Alternative to Best Response and Machine Learning Based Beamforming and Power Adaptation for MIMO Ad Hoc Networks

Decentralized joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment is considered. An important feature of the considered environment is that altering the transmit beamforming pattern at some node generally creates more significant changes to interference scenarios for neighboring nodes than variation of the transmit power. Based on this premise, a good neighbor algorithm is formulated in the way that at the sensing node, a new beamformer is selected only if it needs less than the given portion of the transmit power required for the current beamformer. Otherwise, it keeps the current beamformer and achieves the performance target only by means of power adaptation. Equilibrium performance and convergence behavior of the proposed algorithm compared to the best response and regret matching solutions is demonstrated by means of semi-analytic Markov chain performance analysis for small scale and simulations for large scale networks.
Legitimate Eavesdropping in UAV-Based Relaying System

This letter studies the legitimate eavesdropping with one full-duplex monitor (E) in a suspicious relaying system, where one suspicious transmitter (S) communicates with one suspicious destination (D) with the help of one suspicious unmanned aerial vehicle-based relay (U). Under this setup, E aims to maximize its eavesdropping rate by overhearing the suspicious messages from S/U and optimizing the power of jamming U/D concurrently. However, given specific jamming environment, U could be adaptively deployed to maximize the communication rate of the suspicious system, which brings the fundamental challenge for E to figure out the exact jamming effect. Hence, generally successive convex approximation for determining the corresponding deployment of U and exhaust search over the jamming power is required to achieve the optimal solution. To decrease the high complexity of the optimal strategy, one novel heuristic scheme is further proposed, in which one sub-optimal jamming power is derived immediately. Simulations present the considerable gain of the proposed optimal and heuristic schemes compared to some intuitive schemes.
Link-Level Performance of Rate-Splitting based Downlink Multiuser MISO Systems

This work provides the first link level performance evaluation of the Rate-Splitting (RS) based precoding scheme in a downlink multi-user multiple input single output (MU-MISO) system. Contrary to the existing works on the RS precoding that mostly focused on the sum rate or minimum rate maximization, this work bridges the optimization results with the bit error rate (BER) performance, initiating the RS software implementation. We demonstrate that, in an overloaded scenario, the conventional precoding schemes suffer from the BER error floor that corresponds to their rate saturation, which can be overcome by the RS-based strategy that adding the message decodability of certain users with the interference-limited message rate.