Miracle-dz/UniFAR-FTUs huggingface dataset:
The Evaluation of FFR for Interference Management in Coordinated Hybrid Terrestrial-Aerial Network

Recent works have tried to incorporate unmanned aerial vehicle (UAV) base stations in conventional networks, however, the inter-cell interference mitigation techniques for such networks have not been studied comprehensively. In this paper, we consider a new heterogeneous cellular network, where terrestrial base stations (TBSs) and low altitude platforms (LAPs) collaborate to construct a hybrid network. We believe that it can serve as the stepping stone to a full-fledged coordinated multi-point implementation for 5G and beyond networks. The proposed network is simple, open to improvement and also makes use of available TBS. It promises to increase minimum signal-to-interference ratio (SIR) value of users and also overall throughput by using fractional frequency reuse. The hovering LAP is used for edge users that have lower SIR than TBSs threshold. Furthermore, we simulated a few scenarios, analyzed the effect of radius and UAV altitude, found optimum values for both.
Hop-2 IEEE 802.15.5 Optimization framework for Resource Allocation with Service Differentiation

The shared superframe multihop nature of wireless personal area networks (WPAN s) encounters fundamental challenges to the design of effective and optimal resource allocation algorithms with respect to resource utilization and fairness across different network devices. In this paper, we propose a distributed optimization framework for resource allocation scheme for IEEE 802.15. 5 hop-2 for fairly sharing the channel time resources granted by the reference Mesh Piconet Coordinator (MPNC) to the hop-1 MNPC among hop-2 devices. A NUM based optimization approach is proposed for optimal resource allocation with differentiation between real-time (RT) and non-real time (NRT) devices. Simulation results for studying satisfaction and fairness indexes are conducted.
Compact Analytical Model for Trap-Related Low Frequency Noise in Junctionless Transistors

The aim of this work is to propose a compact analytical model for the Low Frequency Noise (LFN) in Junctionless Nanowire Transistors (JNTs). Since JNTs work differently from inversion mode transistors, the noise is also expected to behave differently. To the best of our knowledge, no analytical models have been presented for LFN in these devices. The proposed model is validated through numerical simulations. Experimental results are also used to demonstrate its applicability.
Research and Application of Face Anti-spoofing Based on Depth Camera

With the wide application of face recognition technology in many scenarios, some cases of property loss and privacy leakage caused by face spoofing attacks have occurred, which has aroused extensive discussion on the security and reliability of face recognition technology. Then research on face anti-spoofing has recently gained a well-deserved popularity. In this paper, we use low-cost infrared depth camera to achieve real-time face anti-spoofing. The proposed method (DMF) combines depth information and motion information of human face. An improved RANSAC is used to eliminate face photos and videos spoofing. Then a new blink detection method is used to eliminate 3D print head spoofing. In addition, previous face spoofing databases generally used 2D videos or photos which lacked depth information, we used infrared depth camera to collect a new 3D face spoofing database (3DFSD), which gathered both depth images and infrared images. Abundant comparative experiments on 3DFSD show that DMF can efficiently eliminate face spoofing attacks.
Neural Network Based Traffic Sign Recognition for Autonomous Driving

Autonomous and self-driving cars represent a certitude for the near future and it is expected to be safer, time saving and to bring in environmental benefits. The road infrastrucuture still embeds traffic signs that have to be recognized with high accuracy by the sensorial system mounted on an autonomous car. This paper deals with a new Traffic Sign Recognitions system based on a pretrained neural network for general objects and retrained for a large number of traffic signs collected in real environment under different metrorological conditions. The proposed Traffic Sign Recognition system is able to classify signs in new scenarios with more than 96% accuracy.
An Efficient Implementation of a TAGE Branch Predictor for Soft Processors on FPGA

Soft processors are becoming a common component on reconfigurable computing like FPGA. For some accelerators, custom logic functions are implemented as processing elements besides the soft processor. Since the resources in FPGA are fixed and limited, it is desired to implement the soft processor with less logical resources as possible. One of the important parts of the processor is an instruction fetch unit whose performance is dependent on branch prediction. Conventional branch predictors like bimodal or gshare are simple to implement but their prediction accuracy is not good enough. On the other hand, TAGE branch predictor has better prediction accuracy but contains complex logic path for branch prediction, which results in the lower operating frequency. In this paper, we propose a branch predictor called pTAGE, which has almost the same prediction accuracy as TAGE and avoids becoming the critical path of the processor. The branch prediction of pTAGE is pipelined, so prediction result is available on each clock cycle. We implement gshare, TAGE, and pTAGE, respectively in Verilog HDL and evaluate their operating frequency and prediction rate based on FPGA implementation. In this result, pTAGE has almost the same prediction rate as TAGE and 1.41 times higher operating frequency than that of TAGE. Also, we evaluate the performance by varying the latency for updating branch prediction, and the evaluation result shows that pTAGE exhibits higher performance in deep pipelined processors than gshare.
Prevalência de sobrepeso em adolescentes: um estudo de base populacional em uma cidade no Sul do Brasil

The prevalence of overweight and its association with demographic, socioeconomic, behavioral, and biological variables were evaluated. The sample included 810 adolescents (10-19 years of age) living in the urban area of Pelotas, a southern Brazilian city. Overweight was defined as a body mass index >= the 85th percentile, according to sex and age, and compared to the First National Health and Nutrition Examination Survey, following the WHO recommendation for adolescents. A clustering sampling strategy was used, and both the crude and adjusted analyses (Poisson Regression) took this strategy into account. The prevalence of overweight was 19.3% (95%CI: 16.6-22.0) and there was no difference between the sexes. The following groups presented a greater probability of being overweight: those classified in the wealthiest socioeconomic groups, those who had dieted to lose weight within the previous 3 months, those who watch 4 or more hours of television per day, and those who have less than 3 regular meals per day. After stratification by gender, high socioeconomic level was associated with greatest risk of overweight among boys. Dieting to lose weight during the previous 3 months, 4 or more hours of television viewing per day, and less than 3 formal meals per day were risk factors for overweight among girls.
Jammer detection in M-QAM-OFDM by learning a Dynamic Bayesian Model for the Cognitive Radio

Communication and information field has witnessed recent developments in wireless technologies. Among such emerging technologies, the Internet of Things (IoT) is gaining a lot of popularity and attention in almost every field. IoT devices have to be equipped with cognitive capabilities to enhance spectrum utilization by sensing and learning the surrounding environment. IoT network is susceptible to the various jamming attacks which interrupt users communication. In this paper, two systems (Single and Bank-Parallel) have been proposed to implement a Dynamic Bayesian Network (DBN) Model to detect jammer in Orthogonal Frequency Division Multiplexing (OFDM) sub-carriers modulated with different M-QAM. The comparison of the two systems has been evaluated by simulation results after analyzing the effect of self-organizing map’s (SOM) size on the performance of the proposed systems in relation to M-QAM modulation.
Avaliação do processo de aconselhamento pré-teste nos Centros de Testagem e Aconselhamento (CTA) no Estado do Rio de Janeiro: a percepção dos usuários e profissionais de saúde

This study sought to evaluate the pre-test counseling process in the HIV Testing and Counseling Centers (CTA) in Rio de Janeiro State, based on the perceptions of users and health professionals. A population survey was performed, based on a structured questionnaire given to a sample of users and counselors of nine CTAs. Quantitative analyses were employed to evaluate the degree of satisfaction in relation to infrastructure indicators of the way patients are received and treated, the user-counselor relationship, and territoriality, accessibility and availability. Among the CTA users interviewed, 58.1% were very satisfied and 38.7% were satisfied with the care received, according to analysis of set of indicators. The majority of health professionals (80.9%) interviewed also declared their satisfaction. Despite the high level of satisfaction, some complaints regarding structural and procedural aspects were identified, which call for the attention of the health managers and professionals for the enhancement of the quality of the service rendered.
Deep Spatio-Temporal Modeling for Object-Level Gaze-Based Relevance Assessment

The current work investigates the problem of objectlevel relevance assessment prediction, taking into account the user’s captured gaze signal (behaviour) and following the Deep Learning (DL) paradigm. Human gaze, as a sub-conscious response, is influenced from several factors related to the human mental activity. Several studies have so far proposed methodologies based on the use of gaze statistical modeling and naive classifiers for assessing images or image patches as relevant or not to the user’s interests. Nevertheless, the outstanding majority of literature approaches only relied so far on the use of handcrafted features and relative simple classification schemes. On the contrary, the current work focuses on the use of DL schemes that will enable the modeling of complex patterns in the captured gaze signal and the subsequent derivation of corresponding discriminant features. Novel contributions of this study include: a) the introduction of a large-scale annotated gaze dataset, suitable for training DL models, b) a novel method for gaze modeling, capable of handling gaze sensor errors, and c) a DL based method, able to capture gaze patterns for assessing image objects as relevant or non-relevant, with respect to the user’s preferences. Extensive experiments demonstrate the efficiency of the proposed method, taking also into consideration key factors related to the human gaze behaviour.
Automatic Generation of Fill-in-the-Blank Programming Problems

In solving programming problems, it is difficult for beginners to create program code from scratch. One way to navigate this difficulty is to provide a programming problem to them which takes a fill-in-the-blank format. In this work, we propose a method to automatically generate programming problems that has two key constituents, selection of exemplary source code and selection of places to be blanks. In terms of selecting exemplary source code, k-means clustering with silhouette analysis in the Online Judge System (OJ) is proposed. Regarding the selection of places to be blanks, a model based on a bidirectional Long Short-Term Memory Network (Bi-LSTM) with a sequential Conditional Random Field (CRF) is proposed. We discuss evaluation of the proposed approach in the context of how fill-in-the-blank programming problems are generated.
Radar to Radar Interference in Common Traffic Scenarios

The use of automotive radar sensors increases in driver assistance systems for future autonomous driving tasks. This leads to increased potential of radar to radar interference and thus performance reduction. This paper investigates the received interference power to provide an understanding of the levels of interference expected under different traffic scenarios. The results of this research provide a basis for further studies which will focus on quantifying the interference probability by considering different waveforms of interferers to identify situations that require interference mitigation. This paper demonstrates that increase in radar penetration rate (RPR) increases the mean value of received interference power while not affecting the maximum value. The results also show that only few closest interferers mainly create these maximum levels which change for different victim radar types. Moreover, another outcome is that increase in relative velocity results in shorter duration of interference power and thus less number of possible interference occurrence.
Sidelobe Reduction by Subarray Stacking for Uniformly Excited mmW Phased Arrays

The performance of multi-user millimeter-wave (mmW) systems is limited by relatively high sidelobe levels (SLLs) of antenna arrays. Per-antenna amplitude control can be used to adjust the amplitudes to reduce the SLL, but the reduction is often achieved at the cost of reduced transmitted power. Large two-dimensional (2D) antenna panels used in mmW phased arrays, however, allow the 2D antenna configuration to be reconfigured to reduce the SLL. In this paper, we present a simplified approach for sidelobe reduction by stacking multiple uniform linear arrays of different size to reduce the sidelobes across the horizontal plane. The approach is based on the relation between the number of antenna elements and the directions of null and sidelobe maxima. The sidelobe reduction is demonstrated by both simulations and measurements. The measurements are carried out in an anechoic chamber at 28 GHz center frequency using a 100 MHz wide modulated 5GNR waveform.
Emotion Recognition Based on Decoupling the Spatial Context from the Temporal Dynamics of Facial Expressions

This paper presents an emotion recognition approach based on decoupling the spatial context from the temporal dynamics of facial expressions in video sequences. In particular, each emotional state is represented as a set of temporal phases, where each temporal phase exhibits different temporal dynamics such as the expressing speed and the variable length of each phase. In this work, we have developed an algorithm for automatically detecting the temporal phases of human facial expressions by employing the concept of mutual information to define a similarity measure among different video frames. Moreover, we have developed a two-layer framework for emotional state recognition. The first layer utilizes the spatial context to classify the frames in an input video into emotional-specific temporal phases using a support vector machine classifier. In the second layer, dynamic time warping is used to classify the sequence of labels associated with the video frames, which is generated in the first layer, into a specific emotional state. In order to validate the performance of the proposed approach, we have conducted extensive computer simulations and the results show an average classification accuracy of 93.53% using the extended Cohn-Kanade facial-expression database.
On the Minimum Range Length for Performing Accurate Direct Far-Field Over-the-Air Measurements

Over-the-air (OTA) performance evaluation requires large investments in anechoic environments. The question of minimizing the test distance is hence critical, and even more in this time where millimeter-wave technologies are about to be largely deployed in 5G devices. A recent publication has identified that direct far-field measurements can be accurately carried out at a much shorter range length than the well-known Fraunhofer distance. This paper introduces a further validation of this reduced distance, by employing an innovative method to simulate spherical measurements with arbitrary DUT, test probes and range lengths. The studies carried out confirm the relevance of this shorter distance, not only for the evaluation of the peak equivalent istropic radiated power (EIRP) or sensitivity (EIS), but also for the total radiated power (TRP) or sensitivity (TIS). In addition, it is demonstrated that the usual assumption that the TRP or TIS measurement is almost independent from the range length is flawed. Two main reasons relating to the test antenna are established which create this dependence: (i) OTA test probes have a finite resolution, and (ii) the probe and instrumentation typically captures the magnitude of two components of the E-field, which are not straightforwardly related to the power density in the near-field.
Nonlinear Control of a Magnetic Levitation System Based on Coordinate Transformations

In this paper, a novel concept of nonlinear controller design is proposed, which is applicable to nonlinear systems subject to external periodic disturbances. At first, by employing a pioneering nonlinear coordinate transformation, a new nonlinear system model with expected dynamic characteristics is derived. Then, a corresponding linear system is achieved by means of linearization techniques at equilibrium points. Next, based on the results of classical linear control theory, a linear quadratic regulator is applied to the resulted linear system. Finally, the nonlinear controller can be obtained through the inverse nonlinear coordinate transformation of the linear feedback controller. The performance of the proposed method is validated on a magnetic levitation system and is compared with the classical linearization feedback control. Simulation results show that the proposed controller design assures enhanced performance in terms of both system stabilization and disturbance attenuation.
A General-Purpose Mapper Module for Adaptive OpenMP Runtime Support

The latest versions of the OpenMP specifications have introduced constructs that enable programmers to utilize heterogeneous compute units alongside the main multicore cpu. They allow offloading specific regions of the program code to any of the available computational devices; the offloaded code may itself generate parallelism by employing suitable OpenMP constructs. While the concept seems ideal, co-processors and accelerators, especially embedded ones, often have limited resources or capabilities to provide efficient OpenMP support. Designing an OpenMP infrastructure for such devices can be a real challenge. A very effective solution has been proposed in the form of compiler-assisted, adaptive runtime support that is tailored to each specific application. In this work, we present a general-purpose mapper module, which has the ability to select the best runtime configuration given a) a set of metrics that profile the application and are obtained by compile-time analysis and b) a set of device-specific decision rules, which an implementor provides for a device, written in a customized language.
Modern Mathematical Methods of Analysis and Forecasting of the Complex Systems Dynamics

Perfection of forecasting is the most important and the only non-objectionable method of “treatment” of emerging imbalances in the implementation of current processes. For this reason, the problem of improving the systems of foresight, modeling options for the development of complex systems remains open, and its solution largely depends on the achievement of a balanced process of future technological development. The article describes the prospects and limits of application of methods in the field of forecasting of complex processes, as well as problems arising in the course of these studies.
Energy Management for Microgrids: a Reinforcement Learning Approach

This paper presents a framework based on reinforcement learning for energy management and economic dispatch of an islanded microgrid without any forecasting module. The architecture of the algorithm is divided in two parts: a learning phase trained by a reinforcement learning (RL) algorithm on a small dataset and the testing phase based on a decision tree induced from the trained RL. An advantage of this approach is to create an autonomous agent, able to react in real-time, considering only the past. This framework was tested on real data acquired at Ecole Polytechnique in France over a long period of time, with a large diversity in the type of days considered. It showed near optimal, efficient and stable results in each situation.
Hybrid Processing for Multi-user Millimeter-Wave Massive MIMO Systems via Matrix Decomposition

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems utilize hybrid beamforming techniques to alleviate the implementation complexity of combining a large number of antennas. In this paper, we propose a hybrid processing algorithm via matrix decomposition for mmWave massive MU-MIMO systems. Both inter-user interference (IUI) and inter-stream interference (ISI) within the user are taken into consideration. We derive a closed-form expression of digital and analog precoder/combiner to achieve nearlyoptimal performance. In the large system analysis, we prove that the proposed algorithm can obtain unconstrained optimal performance when the number of antennas is infinite. The results indicate that the proposed algorithm outperforms other existing hybrid designs in terms of BER and sum-rate.
Optimal Control of Energy Storage Devices Based on Pontryagin's Minimum Principle and the Shortest Path Method

Optimal control strategies for storage devices have been extensively explored in recent years. Two leading approaches are solutions based on dynamic programming and solutions that stem from Pontryagin's minimum principle. Recent studies propose an optimal control strategy for storage devices which is based on the idea of the shortest path: the optimal generated energy must follow the shortest path within two bounds set by the load profile and the device capacity. The current paper continues these studies and shows that the shortest path principle may be derived directly from Pontryagin's minimum principle. This result is of theoretical interest since it demonstrates that the intuitive shortest path method can be easily extended to handle more complex systems. Based on this result we also develop a low-complexity algorithm for calculating the shortest path, which is tested in several case studies.
Study Upon Specific Absorption Rate Values for Different Generations of Mobile Phones by Using a SATIMO-COMOSAR Evaluation Dosimetry System

The problem of the human exposure to different types of electromagnetic field sources is a challenging one and should be considered an up to date issue, especially due to the actual trends and progress in mobile communications technology. The here presented paper proposed a study on the measured values of specific absorption rate (SAR) for three mobile phones, GSM-900 and GSM-1800 MHz bandwidths, using a SATIMOCOMOSAR system, provided by an accredited laboratory. A comparison of the SAR values determined by measurements with those from standards has been also done and the conclusions have been taken for different cases of exposure scenarios.