Miracle-dz/UniFAR-FTUs huggingface dataset:
Human action recognition using an image-based temporal and spatial representation

Researchers have been using different technological solutions (platforms) as intervention tools with children with Autism Spectrum Disorder (ASD), who typically present difficulties in engaging and interacting with their peers. Social robots, one example of these technological solutions, are often unaware of their game partners, preventing the automatic adaptation of their behaviour to the user. Therefore, enriching the interaction between the user and the platform, lightening up the cognitive burden on the human operator, may be a valuable contribution. An information that can be used to enrich this interaction and, consequently, adapt the system behaviour is the recognition of different actions of the user through skeleton pose data from depth sensors. The present work proposes a method to automatically detect in real-time typical and stereotypical actions of children with ASD by using the Intel RealSense and the Nuitrack SDK to detect and extract the user joints coordinates. A Convolution Neural Network learning model trained on the different actions is used to classify the different patterns of behaviour. The model achieved an average accuracy of 92.6±0.5% on the test data. The entire pipeline runs on average at 31 FPS.
A CMOS Balun with Common Ground and Artificial Dielectric Compensation Achieving 79.5% Fractional Bandwidth and <2° Phase Imbalance

This paper presents a compact on-chip balun with a turn ratio of 1:2 for sub-6 GHz applications. Common ground between the primary and secondary windings is designed by utilizing a short transmission line (T-line) to eliminate the imbalance. To further mitigate the imbalance, float metal conductors are used as a part of primary winding for artificial dielectric compensation. The balun is fabricated by using a standard 130-nm CMOS process. The bandwidth of the proposed balun for $\vert \mathrm{S}_{11}\vert < -10$ dB is 2.2-5.1 GHz with fractional bandwidth up to 79.5%. In the operational bandwidth, the maximum amplitude and phase imbalance is 1.5 dB and 2°, respectively. The measured insertion loss is 4.8-5.6 dB (including 3 dB splitting loss) within the frequency range from 2.2 to 5.1 GHz.
Compliance Boundaries of 5G Massive MIMO Radio Base Stations: A Statistical Approach

In this contribution, we focus on the exposure limits and compliance distances of 5G communication systems based on large antenna arrays with high gain and multiplexing capability. In particular, starting from the observation that the antenna array continuously changes its radiation pattern to communicate with a number of user terminals, we develop a simple approach for evaluating a reliable but not overly conservative boundary for the fields radiated by the antenna array. This approach is based on the use of the Normalized Average Power Pattern, and through the paper we show its behavior and demonstrate its usefulness in some cases of interest. Finally, the obtained results are validated by means of an electromagnetic simulation of the antenna and the propagation scenario, that is achieved by means of a ray-launching code.
Research on Direct Position Servo Control of Voice Coil Motor with LCL Filter Based on Active Disturbance Rejection Control

Because of its advantages of small size, high positioning accuracy, simple control, voice coil motor (VCM) is widely used in high-precision positioning systems such as disk drive, medical equipment, precision electronic tubes, etc. Digital control is the most commonly used control method for VCM positioning system, since the digital control method has less loss than the analog control method. However, the problem of large current ripple caused by PWM chopping occurs in digitally controlled system. To deal with this issue, LCL filter is combined with VCM in this paper. The VCM position servo control system with LCL filter turns to a 5th order system. Moreover, LCL filter resonance is a problem that must be considered. In this paper, traditional three-loop structure with PID regulator is first designed for the system, and the parameters tuning and LCL filter resonance suppression are studied. An additional capacitive current sensor is needed in this method to achieve LCL filter resonance suppression. In order to eliminate the additional capacitive current sensor, and further improve the position response speed, this paper proposes a direct position servo control method based on active disturbance rejection control (ADRC). ADRC method can estimate and compensate the disturbances in real time, so the system has better robustness. In addition, LCL filter resonance is suppressed by ADRC active damping, so there is no need for an additional capacitor current sensor.
A Hybrid Localization Algorithm Based on Doppler Shift and AOA for an Underwater Mobile Node

To solve the problem of low accuracy of a mobile node localization in an underwater sensor network, this article proposes a hybrid localization algorithm based on Doppler shift and angle of arrival (AOA) for the underwater mobile node (DAHL). This algorithm utilizes the Doppler frequency shift between the different signals received from the anchor node and the mobile node and the AOA measurement of the signal reflected by the mobile node to estimate the instantaneous position and velocity information of the mobile node through a two-stage algebraic method. In view of the existing measurement errors in the actual situation, the position and velocity estimation errors of the mobile node are optimized by introducing auxiliary parameters. Theoretical analysis and simulation experiments show that the DAHL method can effectively improve the localization accuracy of the mobile node. The localization accuracy of this algorithm can be close to the Cramér-Rao Lower Bound (CRLB) with the condition of small measurement errors.
Effluent Quality Prediction of Papermaking Wastewater Treatment Processes Using Stacking Ensemble Learning

Advanced process modeling methods have been used for prediction and monitoring of key quality indices in wastewater treatment processes. However, single conventional models usually have limited precision accuracy when predicting the effluent indices in papermaking wastewater treatment processes. To achieve a better prediction accuracy and robustness, we propose a stacking ensemble learning (SEL) method which utilizes the advantages of the internal base-learning models. The method combines base-learning algorithms including partial least squares, support vector regression, and artificial neural networks with a meta-learning algorithm, which is a multiple-response linear regression in this work. To evaluate the model performance in practical applications, both real wastewater data and simulation wastewater data are used for modeling. The predicted effluent indices include effluent suspended solid (SS<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {eff}}$ </tex-math></inline-formula>), effluent chemical oxygen demand (COD<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {eff}}$ </tex-math></inline-formula>), effluent ammonia concentration (<inline-formula> <tex-math notation="LaTeX">$\text{S}_{\mathrm {NHeff}}$ </tex-math></inline-formula>), and effluent nitrate concentration (<inline-formula> <tex-math notation="LaTeX">$\text{S}_{\mathrm {NOeff}}$ </tex-math></inline-formula>). Compared with base-learning algorithms and other ensemble learning methods, the results demonstrate that SEL significantly improves the prediction accuracy and reduces the prediction errors, which provides a new way to achieve real-time monitoring of wastewater treatment processes.
A Cluster-Based Energy-Efficient Resource Management Scheme With QoS Requirement for Ultra-Dense Networks

Ultra-dense networks (UDN) have been considered as one of the best ways to improve the network throughput and energy efficiency (EE). However, massive and unplanned deployment of small cells will cause severe intra-tier interference among small cells and then deteriorate the network EE. For this, a Cluster-based Energy-Efficient Resource Management (CEERM) scheme is proposed to mitigate the interference while guaranteeing the quality of service (QoS) of user equipments (UEs) in this paper. Firstly, we propose a cell clustering algorithm to divide small cells into disjoint cell clusters according to the neighboring relationship. Then, the UEs in each cell cluster are further grouped into UE groups with the target of minimizing intra-cluster interference. Finally, a two-step subchannel allocation and non-cooperative game based power allocation scheme is proposed to maximize the network EE. The simulation results show that the proposed scheme CEERM can effectively boost the network EE with low computation complexity.
Learning-Aided Multi-RAT Operation for Battery Lifetime Extension in LPWAN Systems

End-device (ED) lifetime is considered to be a crucial design factor in radio systems for massive machine-type communications. This parameter is heavily impacted by the continuously changing propagation conditions between the ED and the base station. In this paper, to extend the ED lifetime, we consider equipping a single ED with multiple low-power wide-area network (LPWAN) technologies to dynamically select the one with lower energy consumption. To facilitate this process, we propose to employ reinforcement learning (RL) algorithms. Assessing the resultant performance, we conduct two large-scale measurement campaigns that characterize the ED power consumption and the time-dependent propagation conditions for NB-IoT, Sigfox, and LoRaWAN technologies. Our numerical results demonstrate that the designed schemes effectively reduce ED power consumption by timely reacting to the varying radio conditions. Consequently, the ED lifetime expectancy is prolonged by around 10%. For instance, the Thompson sampling technique delivers the most consistent results by outperforming its counterparts and allowing to exploit up to 99% of the theoretical gains while converging over only 25-50 samples.
Analysis and Prediction of Chaotic Time Series Based on Deep Learning Neural Networks

In this paper, we propose a Differencing Long Short-Term Memory (D-LSTM) architecture as an extension of recurrent neural networks. The differencing is the latter value minus the previous value, which can reduce the noise of the original data to make it smooth and improve the prediction accuracy. We design a 3D nonlinear chaotic system and analyze its properties and dynamic behaviors by phase portraits, equilibrium points, Lyapunov exponents, spectral entropy. We study prediction result by change the initial value and the coefficient for our chaotic system. We compare D-LSTM with Adaptive Neuro Fuzzy Inference system (ANFIS) and original Long Short-Term Memory (LSTM), using Root Mean Square Error (RMSE) to measure their performance. The result shows that our model is almost better than others.
Fast True Random Number Generator Based on Chaotic Oscillation in Self-Feedback Weakly Coupled Superlattices

Random numbers play a vital role in communications and cryptography. However, most existing true random number generators have difficulty in satisfying the requirements of high-speed communications due to their complexity and bulkiness, or low speed limitations due to equipment bandwidth. Then, the all-electron true random number generator was presented based on GaAs/Al0.45Ga0. 55As superlattices conducted under direct current bias at room temperature, which not only possesses the characteristics of miniaturization but also generates random numbers at rates up to the Gbit/s. However, the bit rate of random number generators based on superlattices is still much slower compared to chaotic laser random number generators. In order to generate higher-rate random numbers, we modified a DC-excited superlattice and then redirected the signal generated by the superlattice back into itself, thus introducing a self-feedback, and achieved a self-feedback superlattice true random number generator. This improvement makes the superlattice have a more detailed signal shape, a more effective signal amplitude, and with lower power consumption. Therefore, these advances made the self-feedback superlattice more suitable for generating random numbers. Moreover, we propose a new post-processing method, called the adjacent bits reversal exclusive-or. This method can reduce the sequence bias and correlation without discarding any random bit. The random number obtained by the self-feedback superlattice at a sampling rate of 10 GS/s passed the triple standard deviation test and the random number standard test (NIST SP 800-22), indicating that it possessed good statistical properties as a miniaturized random number generator.
A 22-37 GHz Broadband Compact Linear Mm-Wave Power Amplifier Supporting 64-/256-/512-QAM Modulations for 5G Communications

This paper presents a transformer-based broadband compact linear millimeter-wave (mm-Wave) power amplifier (PA) for fifth-generation (5G) communications. A prototype PA is implemented in 90nm SiGe BiCMOS. The PA achieves 17.4dB S21 at 29.2 GHz with a 3dB bandwidth (BW-<inf>3dB</inf>) from 21.8GHz to 37GHz. The PA achieves 20.9/20.3 dBm saturated output power (P<inf>sat</inf>) and 43.7/39.8% peak power added efficiency (PAE<inf>max</inf>) at 24/28GHz. The P<inf>1dB</inf> and PAE at P<inf>1dB</inf> (PAE<inf>p1dB</inf>) are 20.6/19.4dBm and 41.0/38.1% at 24/28GHz. The PA demonstrates 0.6Gb/s 64-QAM signals at -25.1/-25.0dB rms error vector magnitude (EVM) with 12.6/10.5dBm average Pout (P<inf>avg</inf>) and 17.5/12.6% average PAE (PAE<inf>avg</inf>) at 24/27GHz, respectively, without digital predistortion (DPD). The PA also demonstrates 0.9Gb/s 512-QAM signals at -30.1dB rms EVM with 8.9dBm P<inf>avg</inf> and 10.0% PAE<inf>avg</inf> at 24GHz without DPD.
Phylogenetic position of trematode Amurotrema dombrowskajae Achmerow, 1959 (Paramphistomoidea: Cladorchiidae) based on partial 28S rDNA nucleotide sequences

Summary The trematode Amurotrema dombrowskajae Achmerow, 1959, is the type and the only species of the paramphistomoid genus Amurotrema Achmerow, 1959. This intestinal parasite of grass carp (Ctenopharyngodon idella) was introduced with host to the Volga River Delta. These naturalized trematodes were found by the authors in 2014 – 2015. In the present study the phylogenetic position of Amurotrema dombrowskajae is analysed using partial 28S rDNA nucleotide sequences. Bayesian inference and Maximum Likelihood analyses place this species in a clade with representatives of the family Cladorchiidae - Megalodiscus temperatus (Stafford, 1905) and Indosolenorchis hirudinaceus Crusz, 1951. Thus, molecular data are consistent with the traditional view on the position of A. dombrowskajae in this family.
Research on Battery Life prediction Based on Deep Learning

In order to improve the accuracy of life prediction of lithium-ion battery, a life prediction method based on deep belief network and long short-term memory network is proposed. Combined with the capacity data of lithium-ion battery from the Prognostic Center of Excellence (PCoE) of NASA Ames Research Center, discuss the prediction accuracy of the network under different hidden layers, different hidden layer nodes and different optimizers, then choose the best for the fusion model. Combining the above two models, the experimental results show that: the fused model can effectively fit the decline trend of battery capacity, and the remaining useful life prediction error is only 1 cycle. Compared with using DBN or LSTM alone, the prediction accuracy of fusion model is better.
A NEW SPECIES OF CULEX (DIPTERA: CULICIDAE)

The present paper reports the new record of the subgenus Thaiomyia of Culex from China with discription of a new species Culex (Thaiomyia)hainanensis sp.nov.It is closely related to C. (T.) dispectus Bram,1966.As in the latter species,the third segment of the male palpus has no distinctive lanceolate scales and the pecten of siphon is absent.However,the two species can be differentiated as follows:Proboscis with an apparent median joint;Subapical lobe of basiwere with 3 apical rods and a strong subapical seta; 2 strong setae behind the subapical lobe,but without an apico-ventral lobe;Distimere usually with a crest of 1 or 2 barbs;Lateral plate of the phallosome with approximately 8 lateral denticles;Paraproct without lateral arm.Without median joint;Subapical lobe with an apical pair of rods and a subapical rod; an apico-ventral lobe and a smaller rod behind the subapical lobe;With a crest of 5-6 wavelike teeth; Without lateral denticles;With a short nipple-like lateral arm.Head hairs:5-c 12 branched,6-c approximately 9 branched; Lateral comb with 20-30 fringed teeth;Basal tuft of ventral brush usually inserted between the grid and the saddle;Siphonal index variable,ranging from 3-4.5-c 6-8 branched,6-c 5-8 branched;With 10-18 teeth and each tooth with some distinct terminal spines in addition to some lateral spines and fring;Ventral brush all inserted in the grid;Variable,ranging from 2-2.5As stated above,the males and the larva of the two species can be distinguished,but the females could not really be separated.Type specimens:Holotype 1,paratypes 1,2,4 larvae and a larval skin from tree hole cf Diaolaoshan,Hainan Island.Part of type specimens are deposited in the Biology Department of the Kweiyang Medical College.
Millimeter-Wave Line-of-Sight Probability Modeling for Inside Metro Carriages Scenarios

This paper proposes a new Line of Sight (LoS) probability model for metro carriage scenarios to evaluate radio propagation for the future Fifth Generation (5G) Wireless Communications Systems in the Millimeter Wave (mmW) bands. The new model has been defined after 36 measurement campaigns. For each campaign a high precision 3D scenario with a random distribution of passengers and different positions of TX has been used. A comparison between the proposed model and ITU, WINNER A1 and WINNER B3 LoS probability models has been made. As a result, it is shown that the proposed model is the most suitable for this scenario and considerably minimizes the Mean Square Error (MSE) compared to the other models.
Review of Android Malware Detection Based on Deep Learning

At present, smartphones running the Android operating system have occupied the leading market share. However, due to the Android operating system’s open-source nature, Android malware has increased dramatically. Malware can steal user privacy and even maliciously charge fees and steal funds. It has posed a severe threat to cyberspace security because traditional detection methods have many limitations. With the widespread application of deep learning in recent years, the method of detecting Android malware using deep learning has gradually attracted widespread attention from scholars at home and abroad. Although scholars have researched Android malware detection using deep learning, there is currently a lack of a detailed and comprehensive introduction to malware detection’s latest research results based on deep learning. In order to solve this problem, this study analyzes and summarizes the latest research results by investigating a large number of the latest domestic and international academic papers, summarizing malware detection architecture and detection schemes, and analyzing existing problems and challenges. This review will help researchers better understand the research status and future research directions in this field.
A PPG-ECG Combo System for the Monitoring of the Aging State of Arteries

Arterial hypertension is an indicator of cardiovascular pathologies. It causes arterial stiffness which worsens with aging. This paper presents a novel PhotoPlethysmoGraphy-ElectroCardioGraph (PPG-ECG) combo measurement system, using a dedicated signal processing methodology, which aims at the evaluation of the Pulse Wave Velocity in order to obtain clinical parameters of the aging state of the arteries (Arterial Stiffness). Preliminary experimental results are presented, which demonstrates the suitability of the proposed methodology.
Convergence Prediction of Mobile Nodes for Energy Transaction in 5G Network

In a smart city paradigm, fossil-fuel-powered vehicles (internal combustion engine vehicles), hybrid vehicles (such as locomotives), and Electric Vehicles (EV) coexist. Surplus energy (such as regenerative energy) from fossils fuel-powered Vehicles also can act as Mobile Distributed Generators (DG) There can be two conditions under which surplus energy can be discharged from a Mobile DG to a consumer. Mobile DG can deliver surplus energy to a stationary roadside Nano/Microgrid (Vehicle to Grid - V2G). The Mobile DG can also deliver it directly to a mobile EV (Vehicle to Vehicle - V2V), which desperately needs recharge in order to continue its journey. In the later condition, as both energy supplier and consumer nodes are mobile, it is mandatory to bring them at a common spatiotemporal location for efficient energy transactions. In this paper we propose a fog based novel mechanism supported by 5G systems where the interoperability amongst “Resource Producers” like DG and “Consumers” like EV, has been considered whereeither or both stakeholders can be mobile. The dynamic convergence of both consumer and producer is calculated by the proposed algorithm and the best possible meeting point is detected for resource transaction.
Characterizing Latency Overheads in the Deployment of FPGA Accelerators

FPGA hardware accelerators have recently enjoyed significant attention as platforms for further accelerating computation in the datacenter but they potentially add additional layers of hardware and software interfacing that can further increase communication latency. In this paper, we characterize these overheads for streaming applications where latency can be an important consideration. We examine the latency and throughput characteristics of traditional server-based PCIe connected accelerators, and the more recent approach of network attached FPGA accelerators. We additionally quantify the additional overhead introduced by virtualising accelerators on FPGAs.
The Use of Affective Care Robots Calls Forth Value-based Consideration

Care work robotization is socially constructed by several mechanisms, including the law and the codes of ethics shared by occupational groups. Using the dichotomy of effective and affective robots, this study brings novel information to robot acceptance and, particularly, to the values and norms behind care workers’ intentions to use care robots in the future. Data from 407 care workers with care-robot experience were analyzed in regression models. Robots of an effective design were highly accepted by care workers in hospitals and social housing but not as much in home care. Personal values were a significant factor only in the intention to use affective robots, whereas subjective norm had explanatory power regarding affective and effective robots. Thus, as the most consistent result, those respondents who found robot use compatible with the shared norms in their workplaces were more willing to use care robots as a part of their work. Personal values correlated with attitudes toward robots in general, while subjective norm was more strongly and specifically associated with care-robot acceptance. However, considering the maturity of today’s care-robot technology, value-based assessments do not necessarily include anti-technology attitudes but, instead, call for better suiting technology applications.