Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic Agent Movement Prediction Using ResNet-based Model

Autonomous driving is a promising field, which brings conveniences to the life of people and optimizes the operations of the social system. Although many advantages it has, the complexity of autonomous driving hinders the applications of it in practice. autonomous driving is a comprehensive and complex project, which contains lots of difficult challenges. And the traffic agent movement prediction is one of them. In this paper, we regard the traffic agent movement prediction as a regression problem. And a deep neural network model of which the backbone is ResNet101 is proposed to deal with the regression. To demonstrate the efficiency of the proposed method, experiments on Lyft Motion Prediction for Autonomous Vehicles data set are conducted. And the quantitative comparisons of the experimental results indicate that the proposed method is more efficient on the traffic motion prediction than comparing methods.
A Dual-Wideband Circular Polarized Shared-Aperture Antenna for CubeSat Applications

A dual-wideband circular polarized shared aperture antenna for CubeSat applications is presented in this paper. The antenna exploits two distinct components operating at separate bands. The S-band antenna is based on a stacked patch structure. Circular polarization with a single feeding point is achieved by truncating the corners of both patches. The X-band antenna adopts an S-inverted printed dipole where the upper patch of the S-band antenna acts as a ground plane. An alternative technique to feed the printed dipole avoiding the use of a balun is then proposed. The shared aperture antenna shows a -10dB impedance bandwidth from 1.92 to 2.16 GHz and from 7.2 to 10 GHz in S- and X- band, respectively. These performances are able to cover the downlink and uplink frequencies for the Earth Exploration Satellite Services (EESS). An AR below 3dB in the S-band and below 1.5dB in the X-band within the EESS bandwidths is achieved.
Using Machine Learning to Classify Android Application Behavior

Android malware detection has been a major concern in the recent years. Google Play store uses a malware detection system called Play Protect to verify the benignity of the apps before making them available for download. However, a considerable number of apps on Google official store is supplemented with malicious behaviors that bypass Play Protect. To address this issue, we introduce DroidLicious, a solution that focuses on identifying whether an Android app is supplemented with a malware-like behavior. DroidLicious utilizes reliable machine learning classifier to classify the static features of the app, which are extracted using state-of-the-art static taint analysis techniques. DroidLicious’s classifier was trained using static features extracted from 6,538 malware and benign apps of equal distribution. Our experiments proved DroidLicious accuracy in classifying the behavior of Android apps, also that it can improve through re-training using more samples.
Multi-bandwidth NLOS Identification Based on Deep Learning Method

The identification of the propagation conditions of the wireless system, i.e., line-of-sight (LOS) and non-line-of-sight (NLOS), has a significant impact on radio based positioning performance and channel modeling. Traditional methods to identify LOS/NLOS are usually based on extracting features of the channel impulse response (CIR), based on which a decision threshold is used to distinguish between LOS and NLOS conditions. However, channel features of LOS and NLOS are sometimes similar with each other, which usually results in low accuracy of LOS/NLOS identification. In this paper, we use the combined channel feature that is consist of channel state information (CSI) and four characteristics of the channel impulse response as input, and use Long Short-Term Memory (LSTM) to train the learning method to identify LOS/NLOS condition. The proposed method is evaluated based on measured data. The results show that the proposed method achieves an accuracy of 95.477% for LOS/NLOS channel identification.
Challenges toward Low-Power SOT-MRAM

Spin-orbit-torque magnetic random-access memory (SOT-MRAM) equipped with sub-I-V switching voltage [1, 2] is considered to be one of the promising candidates for next-generation low-power, high speed and non-volatile embedded cache memory applications. To fulfill these performance requirements, however, there are many technical bottlenecks to be conquered, such as SOT efficiency and scalability. This paper presents the recent progress on SOT-MRAM exploration of CMOS compatible high spin-Hall conductivity materials and structures.
Reducing Social Media Users’ Biases to Predict the Outcome of Australian Federal Election 2019

Several online social networking sites (OSNs) are being used as a medium of expressing any ideas, opinions, thoughts towards any regular or special issues and also for support or even oppose any social or political matters at the same time. At the age of this modern technology, in any country, people would like to post their views regarding any political party during the election period on such emerging OSNs in order to demonstrate their stands upon them. In this paper, we incorporated the tweets relevant to the Australian federal Election 2019, with a view to serve our primary purpose of predicting the outcome of it. We aggregated two efficacious techniques to extract the information from a large Twitter dataset to count a virtual support for each corresponding political group and propose an approach of reducing users’ biases in OSNs to predict outcome of the election more efficiently. Our investigation finds close relevance with the original results of the election published by the Australian Electoral Commission.
64-QAM LTE signal transmission at 25 GHz over hybrid SSMF and non-uniform turbulent FSO channel

This contribution describes the impact of non uniform thermal-induced turbulence on 64-QAM LTE signal transmission at 25 GHz millimeter wave (mmW) signal along hybrid RoF/RoFSO links. Microwave photonic signal generation has been employed by using the optical frequency doubling with intensity modulator biased at the minimum transmission point. The optical and electrical power budgets of different scenarios have been defined and error vector magnitude (EVM) performance has been measured. It is shown that the uniform scenario with high turbulence in the middle of the link is the one with greater impact on the quality of the received data.
A Dual Band Dual-Polarized Sub-6GHz Base Station Antenna

This paper proposes a dual band dual-polarized base station antenna. The radiator of this antenna is a pair of modified crossed dipoles, which helps the antenna to operate at two frequency bands. A flat reflector instead of a shaped one is used to shape the radiation pattern of this antenna. The simulation and measurement results show that this antenna has good performance (VSWR < 1.5 and isolation > 20 dB) at the 4G band and part of the 5G band. It also has good gains in the desired frequency bands.
A Novel Approach to Non-Invasive Blood Glucose Sensing Based on A Defected Ground Structure

In this study, we explore a novel approach to measure blood glucose concentration in a non-invasive way using a compact defect ground structure (DGS) filter. This proposed approach is cost-effective, compact, and convenient. The sensor was designed using CST Microwave Studio. The size of the fabricated sensor is 40×40× . 883 mm3, which is suitable for hand-held use. The proposed sensor operates at 1.88 GHz and was able to achieve a good sensitivity relative to the change of glucose concentration. The proposed sensor is a potential candidate for non-invasive blood glucose monitoring applications.
An Energy Spectrum Algorithm for Wind Direction Retrieval From X-Band Marine Radar Image Sequences

In this study, an energy spectrum (ES) algorithm is proposed to retrieve wind direction from X-band marine radar image sequences. This algorithm is based on utilizing the occlusion area zero-pixel percentage to distinguish rain-free and rain-contaminated radar data. And then the rain-contaminated images are detected and discarded. The effect of radar radial attenuation in radar image sequences is modified by the piecewise fitting technique. Wind direction is determined from rain-free and radial correction data, based on the ES of small-scale wind streaks. The ES of small-scale wind streaks is obtained by establishing an ES scale separation filter. Based on the wind streak characteristics, a 2-D fast Fourier transform is used to obtain the ES of radar images. The wind streak characteristics are derived from the distribution of the azimuth normalization radar cross section. The proposed algorithm is tested using data collected from X-band radar images and in situ anemometer data from the coast of the East China Sea. Compared with the anemometer data, after using the proposed algorithm, the root-mean-square difference for wind direction is 12.13°, which is an acceptable result for engineering application.
On the Modelling of the NLOS First Multi-path Component in Stochastic Spatial Channel Models

Positioning and localization is one of the key features in the 5G and beyond wireless networks. Since accuracy strongly depends on the channel conditions, channel models are of special importance for the testing and development of such applications. While several localization methods are based on the time of arrival (ToA) and time-difference of arrival (TDoA), stochastic spatial channel models (SCMs) as the 3GPP don’t consider absolute values on the delays of the non-line of sight (NLOS) multi-path components (MPCs), making these models not appropriate for these applications. Therefore, we investigate the time and angular characteristics of the first multi-path component (FMPC) using multi-band ray tracing (RT) simulations in order to extend stochastic SCMs to support localization applications in NLOS.
Efficient DOA Estimation Method for Wideband Uncorrelated Signals

An efficient DOA estimation method for wideband uncorrelated signals is presented in this paper. Compared with the focusing Khatri-Rao subspace method, the computation burden of the method degraded greatly due to using the technique of uniform focusing. Moreover, the method can resolve more signals than the number of array elements and have higher performance. Simulation results demonstrates the effectiveness and efficiency of the method.
Automatic prediction of learning styles in learning management systems: a literature review

The Web-based learning environment promises to have a profound impact on learning processes. One way in which this field of research is bringing about beneficial effects for learners is through learner modelling that offers the content and learning resources to learners depending on their learning needs and personal traits, often in an interactive manner. A number of studies have focused on the integration of learner’s learning styles and other personal traits in adaptive Web-based learning environments utilizing computer-based techniques. An important factor in these studies is to automatically predict the learning style. This paper provides a review of recent studies in learning styles modeling and prediction in e-learning systems. We identified three research questions and employed a systematic literature review technique to select and critically analyze the most relevant and important papers in automatic prediction of learning styles. An overview of learning style theories and learner modeling is provided followed by a discussion on learning styles theory selection, predictors and dimensions, and prediction and integration techniques.
A Conversion Mode Reconfigurable SAR ADC for Multistandard Systems

A single-channel reconfigurable successive approximation register (SAR) analog-to-digital converter (ADC) is presented, which features its speed expanding with conversion mode. The reconfigurable capacitor digital to analog converter (CDAC) is proposed to achieve multiple conversion modes without wasting capacitance. In 1-b/cycle conversion mode, the proposed ADC can achieve the sampling rate of 60-Ms/s and 9-b resolution. Based on the 2-b/cycle conversion mode, the proposed ADC can double the sampling rate and be reconfigured as a 120-MS/s, 8-b converter. Besides, the detection skip algorithm and charge sharing technology are involved to remove the precharge operation time and reduce the switching energy consumption. Moreover, the control logic is optimized to minimize the chip area and matches the reconfigurable characteristics well. A prototype ADC is fabricated in the 180-nm standard CMOS process, which achieves the 54.1-/46.7-dB signal-to-noise-plus-distortion ratio (SNDR) at 60-/120-MHz sampling frequency with the power consumption of 1.9/3.5 mW. The prototype ADC achieves a peak figure of merit (FoM) of 77-fJ/Conv.step at 2-b/cycle conversion mode. The ADC core occupies an active area of only 0.12 mm2.
Comparison of Split-Ring Resonator Composite Periodic Media based on Numerical Eigensolutions

In this work we study the behavior of composite periodic media consisting of split-ring resonators by means of the solution of eigenvalue problems. Initially, we compare the dispersion diagrams of Edge-Coupled Split-Ring Resonator (EC-SRR) and Broadside-Coupled Split-Ring Resonator (BC-SRR) media for the case of in plane propagation. Subsequently, we present the comparison results between the BC-SRR and its complementary counterpart which exhibit magnetic and electric resonances, respectively, at the same frequency zones. Field distributions of the returned modes are illustrated and their characteristics are studied with respect to their expected behavior extracted from analytical models in literature.
Augmenting Few-Shot Learning With Supervised Contrastive Learning

Few-shot learning deals with a small amount of data which incurs insufficient performance with conventional cross-entropy loss. We propose a pretraining approach for few-shot learning scenarios. That is, considering that the feature extractor quality is a critical factor in few-shot learning, we augment the feature extractor using a contrastive learning technique. It is reported that supervised contrastive learning applied to base class training in transductive few-shot training pipeline leads to improved results, outperforming the state-of-the-art methods on Mini-ImageNet and CUB. Furthermore, our experiment shows that a much larger dataset is needed to retain few-shot classification accuracy when domain-shift degradation exists, and if our method is applied, the need for a large dataset is eliminated. The accuracy gain can be translated to a runtime reduction of $3.87\times $ in a resource-constrained environment.
A Hybrid Short-Term Load Forecasting Model Based on CatBoost and LSTM

In this paper, a hybrid model is proposed to forecast short-term load. The hybrid model consists of two layers. In the first layer, multiple CatBoost and LSTM models are trained using K-Fold cross validation to get the out-of-sample predictions. In the second layer, use ridge regression as stacking’s combiner algorithm to combine the forecasting results of the first layer’s models. The proposed model is evaluated on the load data collected from a transformer substation in Hangzhou, and is compared with the existing models based on Gradient Boosting Decision Tree, Long Short-Term Memory and CatBoost. The results demonstrate that the proposed model outperforms all the mentioned existing models.
Using AFM Measurements for Failure Indication During High-Cycle Fatigue Testing of thin Metal Films on MEMS Cantilevers

We use Si MEMS cantilevers as sample carriers to gain high cycle fatigue data for thin metal films by accelerated stress testing. A typical number of 107 cycles can thus be achieved in a couple of hours. A closed loop control using laser beam deflection allows to keep the load on the thin film constant while in parallel its degradation is monitored continuously by recording resonance frequency, thin layer electrical resistance and surface roughness, the latter ex situ by AFM. We show a correlation between those three failure indicators and motivate a relationship with the failure parameter within a physics-of-failure based reliability paradigm. The proposed method opens an introspect view on the degradation behaviour of thin metal films as well as a rapid statement on their reliability under vibration loading at different temperatures. In this paper, the method is exemplified on the disruption of $1.2 \mu \mathrm{m}$ thin sputtered Aluminium layers.
Adversarial Training for Underwater Target Recognition in Complex Marine Conditions

Studies on underwater target recognition have been making impressive progress recently due to the application of many deep learning methods. However, when the test set and training set contain different marine background noises, the performance of the trained model deteriorates. According to our observations, such problem cannot be solved by traditional noise reduction techniques and alleviating overfitting. Considering the complexity of marine background noise and the dissimilarity in transmission channels under different marine conditions, we first compare various marine conditions to different generalized channels so that this problem can be compared to channel mismatch in the field of speaker recognition. Inspired by domain adaptation and channel compensation, we propose a Generalized Channel-Invariant Network (GCIN), which uses a gradient reversal layer (GRL) and an adversarial training framework to eliminate the dissimilarity in the received data caused by various marine conditions and then a more robust model is obtained. Experiments on two multi-target and multi-marine-condition datasets confirmed that compared with other CNN-based methods that do not use GRL, GCIN can not only effectively alleviate the mismatch problem under marine conditions within given range, increase by 15% when the training set and test set are mismatched, but also improve the accuracy by at least 2.2% when two sets are matched.
A Broadband, Dual-Linear Polarization- Reconfigurable Metasurface Antenna with Characteristic Mode Analysis

A low-profile, broadband metasurface antenna (MSA) with reconfigurable dual-polarization diversity is developed. The developed MSA comprises a 4×4 grid-slotted patch array, two pairs of orthogonally feeding dipole strips, two vertically grounded and orthogonally placed microstrip Γ- shaped feeding strips, a reconfigurable feeding network, and an intact ground plane. It is demonstrated that the strip- excited MSA excites three resonant modes to provide a wide operational bandwidth based on the characteristic mode analysis. The simulated results indicate that three modes with broadside radiation patterns are simultaneously excited to create a wide impedance bandwidth of 32 % with an average gain value of 9.5 dBi at both polarization states.
Indoor Positioning via Artificial Magnetic Fields

Indoor positioning approaches are often based on electromagnetic wave signals with high frequency. However, signal reflections and the corresponding signal superpositions are challenging. As a result, there is still no indoor positioning technology established in commercial products, such as smartphones or smartwatches. Recent publications have shown that this indoor positioning problem can be addressed by using artificial magnetic fields. However, the cubic attenuation of the magnetic field challenges technical implementations and limits the ranges. In this work, we propose an approach striving to address these issues of magnetic indoor positioning via a simple electromechanical single-anchor concept in combination with a small wearable sensor circuitry. The terrestrial magnetic field together with amplitude and phase measurements resolves the position and the orientation of the tag. The proposed indoor positioning approach via artificial magnetic fields is tested in a corridor for a spatial region of approximately 100 m2. The mean positioning error in this experiment was calculated to be $\mu =1.0\pm 0.81\,\,\mathrm {m}$ . The proposed indoor positioning approach via artificial magnetic fields and the corresponding measurement system can, therefore, be useful alternatives when considering applications in which simple implementation is more important than positioning precision.
Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

Mood state assessment (MSA) is increasingly important for diagnosis and treatment of depression. Recent years, many approaches have been proposed for the process of MSA. When using a single approach for MSA, the user often has to deal with possible noisy data and unacceptable error rates. Novelty: In order to improve the accuracy of MSA, in this paper, we propose a novel high-level information fusion method for determining the MS of users by fusing physiological data, such as heart rate and brainwave information collected through a wearable device, and psychological data collected through a monthly mood chart. The multifaceted information must be collected and analyzed simultaneously. Contribution: In the inference process of proposed framework, we adopted a Bayesian Network (BN) to perform high-level information fusion. We exploited various evaluation approaches to evaluate the performance of the proposed approach. Result: We have conducted experiments using two datasets and evaluated the performance using various factors. The results show that the proposed method (7-M Bayesian Fusion) is superior to other methods averagely 9.48 % improvement in most evaluation factors. It reveals that the proposed approach is efficient in fusing the MS information required for accurate diagnosis of depression compared with those approaches without fusion approach or with few information fusing.
Vehicle Flow Statistics System in Video Surveillance based on Camshift and Kalman Filter

With the rapid development of the modern economy, transportation safety and security are becoming critical. The standards for the management of transportation systems are also becoming higher and higher. The modern real-time control technology is integrated into the real-time control of the intelligent traffic system. This paper establishes a smart transportation system that uses traffic pavement cameras to detect traffic flow in a specific period. The system used the background difference method based on the Gaussian model and the Camshift algorithm based on the Kalman filter. It is mainly divided into background modeling, foreground detection, vehicle tracking, and vehicle flow calculation. After background detection, remove the background parts and then use foreground detection to accurately extract different vehicle types of various models that need to be processed. Through tracking and scientific statistics of the detected targets, real-time traffic flow data is calculated. The traffic flow changes are intuitively fed back to the system users in a line chart. Thus significantly reduce the manual part’s workload, effectively improve the efficiency and effectiveness of traffic management.
A Cylindrical Coaxial-fed Resonant Cavity Antenna with Off-axis Beaming for 5G Applications

This paper presents a resonant cavity antenna (RCA) with a directive tilted beam at 28 GHz. A cylindrical waveguide fed by a coaxial probe is used as the main radiator to make a tilted beam. Besides, a proper single-layer partially reflective surface (PRS) is used to enhance the performance of the RCA structure in terms of making a directive pencil beam. A tilted beam at the angle of θ = 300 with a high gain feature is achieved over the desired bandwidth from 27.8 GHz to 28.5 GHz. The maximum gain is 15.8 dBi at 28 GHz. The proposed antenna is suitable for the millimeter wave wireless communications, radar systems and tracking technologies.