Miracle-dz/UniFAR-FTUs huggingface dataset:
Circularly-Polarized Circular Meandered Microstrip Antennas for WLAN Applications

A meandered quarter-mode of the circle with arc-shaped slots and a probe-feed is proposed to produce CP radiation. Two antennas are presented, in which, one is with two arc-shaped slots and the other is having 3-arc-shaped slots to control the return-loss at the phase-shift variation of two orthogonal-modes. The 10dB Bandwidth of the Antenna-1 (A1) is 1.24GHz (4.52-5.76GHz) and Antenna-2 (A2) is 1.21GHz (4.5-5.71GHz). The 3dB AR Bandwidth of A1 is 200 MHz (4810-5010 MHz) and A2 is 120MHz (4830-4950 MHz). The gain obtained for both antennas is 5dBic. The proposed antennas are fabricated and the simulation analysis is presented.
A Multimodal Feature Fusion-Based Method for Individual Depression Detection on Sina Weibo

Existing studies have shown that various types of information on the online social network (OSN) can help predict the early stage of depression. However, studies using machine learning methods to accomplish depression detection tasks still do not have high classification performance, suggesting that there is much potential for improvement in their feature engineering. In this paper, we first construct a dataset on Sina Weibo (a leading OSN with the largest number of active users in the Chinese community), namely the Weibo User Depression Detection Dataset (WU3D). It includes more than 10,000 depressed users and 20,000 normal users, both of which are manually labeled and rechecked by specialists. Then, we extract text-based word features using the popular pretrained model XLNet and summarize nine statistical features related to user text, social behavior, and pictures. Moreover, we construct a deep neural network classification model, i.e. Multimodal Feature Fusion Network (MFFN), to fuse the above-extracted features from different information sources and further accomplish the classification task. The experimental results show that our approach achieves an F1-Score of 0.9685 on the test dataset, which has a good performance improvement compared to the existing works. In addition, we verify that our multimodal detecting approach is more robust than multimodel ensemble ones. Our work could also provide new research methods for depression detection on other OSN platforms.
Research on Key Algorithms of Visual Simulation System for Surface Dust Concentration in Virtual Campus

In the visual simulation of surface dust concentration in virtual campus, the concentration data of each grid point should be obtained. Because the surface dust concentration data grid points are unknown, so through the discrete sample point grid points around the known surface dust concentration properties of the unknown point grid point value interpolation estimation. Therefore, it is very important to select an interpolation algorithm that can satisfy the requirement of the accuracy of the surface dust concentration and have high computational efficiency in the interpolation process of unknown points. According to the data characteristics of virtual campus surface dust concentration value, this paper proposes an inverse distance mixed interpolation algorithm for interpolation by combining the advantages of the inverse distance weighted interpolation method and the kriging interpolation algorithm, and compares the calculation results of inverse distance mixed interpolation with the traditional interpolation algorithm. The results show that inverse distance mixed interpolation algorithm not only has high accuracy, but also runs efficiently.
An Experimental Analysis of Classification Techniques for Domain Generating Algorithms (DGA) based Malicious Domains Detection

In modern days, there has been a significant surge of Domain Generating Algorithms (DGAs) based various types of cyber attacks executed by adversaries to facilitate covert server communication with the help of botnets. Such algorithms provide attackers a plethora of malicious domain names from which a subset of domain names are selected, and hence, common choices of defensive techniques, such as, blacklisting, reverse engineering, sinkholing, and preemptive registration of domains, become highly ineffective. In order to combat this dreadful situation, academia and industry based security researchers and network defenders in cyber realm have been utilizing machine learning based techniques to discover unseen malicious domain names. In our study, we present a unique experimental analysis of 13 state-of-the-art classification techniques to analyze the effectiveness of such classifiers on a large, diverse category of DGA produced malicious domain names’ dataset having 80 different DGA families. We incorporate three text feature extraction methods, such as, unigram, bigram, and trigram, to explore the experimental findings to cover different aspects as well as report the performance results of each compiled machine learning model in terms of accuracy, precision score, recall score, and F1 score. We illustrate all the built models’ performances in a tabular view for the readers to best compare one model with another in a variety of experimental settings we design.
Laser Ultrasound in Transmission Mode for Defect Detection of Additive Manufactured Parts

Laser ultrasonic non-destructive inspection may be difficult to detect small defects inside additive manufactured components due to weak longitudinal waves propagation. This paper proposes a defect detection method of using laser ultrasonic transverse waves in transmission mode. This method uses a scanning procedure of synchronously moving the laser ultrasound generator and the ultrasound detector on the opposite side of the sample. The cross-correlation algorithm is used to calculate the time delay of the transmitted transverse wave at each scanning point. Finally, the location of the internal defect can be estimated by considering the thickness of the sample and the distance between the two maximum signal delay points in the left scanning and the right scanning. The numerical simulation using finite elements method and the experimental results show that this method can successfully identify the internal defect of 1 mm diameter inside the additive manufactured 316L stainless steel components.
Deep Convolutional Neural Network Based Sleep Apnea Detection Scheme Using Spectro-temporal Subframes of EEG Signal

Sleep apnea, a common sleep disorder, has been affecting millions of people all over the world. For automatic detection of sleep apnea from various bio-signals, the Electroencephalogram (EEG) signal is getting more attention because of its physiological interpretation with this disease. In this paper, a patient independent sub-frame based approach for the automatic detection of apnea frames using only EEG signal is proposed. Here instead of directly using a whole frame of EEG data, spectro-temporal subframes are used that are obtained by first extracting frequency band limited signals and then dividing each of them into smaller subframes. Next the extracted subframes are fed into the proposed local convolutional neural network (CNN) blocks. The local features thus produced are then processed using the proposed global CNN block to obtain global features. These features are optimized using deep neural network classifier. The method is evaluated on multiple patients taken from a publicly available database. From extensive analysis it is found that the proposed method offers consistently significant performance in terms of accuracy, sensitivity and specificity. The proposed scheme has the potential to be used for the better detection of sleep apnea in real life application.
A design of dual-band dipole antenna with reflector and FSR using genetic algorithm

A 2 GHz / 3.5 GHz dual-band vertically polarized dipole antenna equipped a reflector and frequency selective reflector (FSR) is considered in this study. The problem to design the antenna is that there are many structural parameters for designing the antenna. This paper reports the results of finding the structural parameters that satisfy the antenna performance required for mobile base station antennas by applying a genetic algorithm.
APSO-based Optimization Algorithm of LSTM Neural Network Model

For the multi hidden layer LSTM recurrent neural network, the updating of its weights and thresholds depends on the gradient descent algorithm, the convergence speed of the model is slow, and the weight calculation of the network nodes is prone to local extremum, which leads to the global optimization of the LSTM neural network model and the decline of the generalization ability of the network model, which limits the application of LSTM recurrent neural network. Therefore, this paper proposes an improved LSTM neural network model based on APSO algorithm. In this model, the root mean square error is designed as an appropriate value function, and the APSO algorithm is used to construct an optimization strategy to globally optimize the weights of each neuron node, so as to improve the generalization and prediction performance of the model. The experimental results of classic data market and UCI data sets show that the prediction accuracy of APSO-LSTM model is significantly improved compared with the traditional LSTM model, which verifies the effectiveness and practicability of APSO-LSTM model.
OAM-based Imaging with Cylinder-shaped Arrays

Recent years, the electromagnetic (EM) wave carrying orbital angular momentum (OAM) has been applied for radar imaging. The dual relationship between the OAM index and the azimuthal angle can be used to reconstruct target’s profiles in the azimuthal dimension. In this paper, the OAM-based imaging is studied to reconstruct the three-dimensional information by exploiting the tomographic technique. The wavefront and intensity distributions of the generated OAM beams are analyzed firstly. Subsequently, the imaging model of the proposed imaging scheme is derived. And, the imaging approach is developed to obtain the target’s position in range, azimuth and elevation dimensions. Simulation results show good agreement with theoretical analyses. This work can advance the development of three-dimensional imaging technology based on OAM beams.
Task Classification Unloading Algorithm For Mobile Edge Computing in Smart Grid

With the rapid development of smart grid, the data generated by grid services are growing rapidly, and the requirements for time delay are becoming more and more stringent. The storage and computing capacity of the existing terminal equipment can not meet the needs of high bandwidth and low delay of the system at the same time. Fortunately, mobile edge computing (MEC) can provide users with nearby storage and computing services at the network edge, this can give an option to simultaneously meet the requirement of high bandwidth and low delay. Aiming at the problem of service offload scheduling in edge computing, this paper proposes a delay optimized task offload algorithm based on task priority classification. Firstly, the priority of power grid services is divided by using analytic hierarchy process (AHP), and the processing efficiency and quality of service of emergency tasks are guaranteed by giving higher weight coefficients to delay constraints and security levels. Secondly, the service is initialized and unloaded according to the task preprocessing time. Finally, the reasonable subchannel allocation is carried out based on the task priority design decision method. Simulation results show that compared with the traditional approaches, our algorithm can effectively improve the overall system revenue and reduce the average user task delay.
A Conformal UWB Dual-Polarized Antenna for Wireless Capsule Endoscope Systems

A conformal ultrawideband (UWB) dual-polarized capsule antenna is designed for ingestible wireless capsule endoscope (WCE) systems. To the best of our knowledge, this is the first time to study the UWB dual-polarized antenna for a capsule endoscope system. Dual polarization is realized by a step-shaped monopole and an irregular rectangular loop antenna, which are printed on the top and bottom side of the dielectric substrate, separately. The simulated antenna has very wide –10 dB impedance bandwidth of 2.15–14.75 GHz, which can cover 2.4–2.48 GHz, 5.725–5.875 GHz Industrial, Scientific, and Medical (ISM) band, 3.5–4.5 GHz impulse radio UWB (IR-UWB) and 3.1–10.6 GHz UWB. Moreover, because of the orthogonally polarized waves generated by two types of antennas, the simulated isolation between them is better than 40 dB in the whole band. Considering the application of the antenna in the wireless body area network system, this design focuses on 1–7 GHz frequency band, especially 4 GHz IR-UWB. An antenna prototype is fabricated and measured, and the measured S-parameters and radiation pattern demonstrate a reasonable agreement with the simulation results. Furthermore, specific absorption rate and link budget of the antenna are analyzed.
Effect of Multi-Scale Decomposition on Performance of Neural Networks in Short-Term Traffic Flow Prediction

Numerous studies employ multi-scale decomposition to improve the prediction performance of neural networks, but the grounds for selecting the decomposition algorithm are not explained, and the effects of decomposition algorithms on other performance of neural networks are also lacking further study. This paper studies the influence of commonly used multi-scale decomposition algorithms including EMD (Empirical Mode Decomposition), EEMD(Ensemble Empirical Mode Decomposition), CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), WD (Wavelet Decomposition), and WPD (Wavelet Packet Decomposition) on the performance of Neural Networks. Decomposition algorithms are adopted to decompose traffic flow data into component signals, and then K-means is used to cluster component signals into volatility components, periodic components, and residual components. A Bi-directional LSTM (BiLSTM) neural network is adopted as the standard model for training and forecasting. Finally, three metrics, including prediction performance, robustness, and generalization performance are proposed to evaluate the influence of the multi-scale decomposition algorithm for neural networks comprehensively. By comparing the evaluation results of different hybrid models, this study provides some useful suggestions on proper multi-scale decomposition algorithm selection in short-time traffic flow prediction.
A Design of Broadband 3-D-Printed Circularly Polarized Spherical Luneburg Lens Antenna for X-Band

This letter presents a broadband 3-D-printed circularly polarized (CP) spherical Luneburg lens (LL) with the linearly polarized feed antenna for the X-band. In order to implement spatially continuous varying effective refractive index (RI), the proposed LL is divided to many unit cells and their effective RI is simulated and calculated by effective medium theory. Since the effective RI of unit cells is anisotropic for two orthogonal linearly polarized (LP) waves, the proposed LL is an LP to CP polarizer realized by properly selecting the radius of the LL. The whole structure is fabricated by low cost 3-D-printing (3DP) technic, which is fed by an LP antenna source rotated to a certain angle with respect to the LL around the normal. The measured results indicate that the voltage standing wave ratio (VSWR), axial ratio, and gain enhancement are lower than 2, less than 2.8 dB, and more than 10 dB at X-band, respectively. The results of measurement agree well with the simulation.
COPSS-lite: A Lightweight ICN based Pub/Sub System for IoT Environments

Content Centric Networking (CCN) and Named Data Networking (NDN) are popular ICN proposals that are widely accepted in the ICN community; however, they do not provide an efficient pub/sub mechanism. Hence, a content oriented pub/sub system named COPSS was developed to enhance the CCN/NDN protocols with efficient pub/sub capabilities. Internet houses powerful devices like routers and servers that can operate with the full-fledged implementation of such ICN protocols. However, Internet of Things (IoT) has become a growing topic of interest in recent years with billions of resource constrained devices expected to connect to the Internet in the near future. The current design to support IoT relies mainly on IP which has limited address space and hence cannot accommodate the increasing number of devices. Even though, IPv6 provides a large address space, IoT devices operate with constrained resources and hence, IPv6 protocol and its headers will induce additional overhead for their operation. Interestingly, we observed that IoTs are information centric in nature and therefore, ICN could be the more suitable candidate to support IoT environments. Although NDN and COPSS are designed for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. Therefore, CCN-lite was designed to provide a light weight, inter-operable version of the CCNx protocol to support the IoT devices. However, we show that communication in the IoT networks resemble pub/sub communication paradigm. However, CCN-lite like its ancestors (CCN/NDN) lacks the support for an efficient pub/sub mechanism while COPSS cannot be directly applied to the constrained IoT networks. Therefore, in this work, we develop COPSS-lite, an efficient and light weight implementation of pub/sub along with multi-hop routing to support the IoT networks. Essentially, COPSS-lite enhances CCN-lite with pub/sub capability with minimal overhead and further enables multi-hop connections by incorporating the famous RPL protocol for low power and lossy networks. Through evaluation using the real world sensor devices from the IoT Lab, we demonstrate the benefits of COPSS-lite in comparison with stand alone CCN-lite. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and significantly improves the performance of constrained devices in the IoT environments.
Influence of Armband Form Factors on Wearable ECG Monitoring Performance

In the current state of innovation in wearable technology, there is a vast array of biomonitoring devices available to record electrocardiogram (ECG) in users, a key indicator of cardiovascular health. Of these devices, armband form factors serve as a convenient all-in-one platform for integration of electronic systems; yet, much of the current literature does not address the appropriate electrode location nor contact pressures necessary to achieve reliable system level ECG sensing. Therefore, this paper will elucidate the role of electrode location and contact pressure on the ECG sensing performance of an electronic textile (E-textile) armband worn on the upper left arm. We first carry out an ECG signal characterization to validate the ideal armband electrode placement necessary to measure high quality signals without sacrificing practical assembly of the armband. We then model and experimentally quantify the contact pressure between the armband onto the upper arm as a function of armband size, a critical parameter dictating skin-electrode impedance and ECG signal quality. Finally, we evaluate how the size of the armband form factor affects its ECG sensing performance. Our experimental results confirm that armbands exhibiting modeled contact pressures between 500 Pa to 1500 Pa can acquire ECG signals. However, armband sizes exhibiting experimental contact pressures of 1297 ± 102 Pa demonstrate the best performance with similar signal-to-noise ratios (SNR) compared to wet electrode benchmarks. The fundamental design parameters discussed in this work serve as a benchmark for the design of future E-textile and wearable form factors with efficient sensing performance.
Multilabel Emotion Detection from Bangla Text Using BiGRU and CNN-BiLSTM

Bangla is a popular language spoken by millions of people worldwide. These people use Bangla to convey their emotions. In addition, many non-natives but enthusiastic people in Bangla also want to interpret or communicate these emotions using the translator service. It would be nice to have a way out of the text to detect these feelings. Aiming this target, we have introduced two approaches to identify the emotion from a Bangla text. Taking deep learning as a cornerstone, we have developed two models to predict the affection of Bangla text. These two-prediction model has accounted for six identical emotion concerned labels (আন, বষতা, ভয়, রাগ, ভালবাসা, আয) to predict. To accomplish this, we relied on the Bidirectional Gated Recurrent Unit called BiGRU and the combination of the most classical deep learning algorithm, Convolutional Neural Network, and Bidirectional Long Short-Term Memory named CNN-BiLSTM. After exploiting these two algorithms, we calculated accuracy, precision, recall, and F1-score to evaluate their output. Since the dataset was assembled using a Google translator, those built models display promising performance for any Google Translator’s translated extensive Bangla text.
Adaptive Fuzzy Finite-Time Command Filtered Impedance Control for Robotic Manipulators

In order to improve the security and compliance of physical human-robot interaction (pHRI), an adaptive fuzzy impedance control for robotic manipulators based on finite-time command filtered method is proposed in this paper. Firstly, robots usually encounter system uncertainties in practical applications, and the adaptive fuzzy control is introduced to approximate the system uncertainties. Secondly, the finite-time control method is used to improve the interaction performance of the system. Then, the command filtered control technique is used to deal with the “computational complexity ” of traditional backstepping. Finally, simulations are conducted to illustrate the effectiveness of the proposed control method in physical human-robot interaction.
Evaluating Guided Policy Search for Human-Robot Handovers

We evaluate the potential of Guided Policy Search (GPS), a model-based reinforcement learning (RL) method, to train a robot controller for human-robot object handovers. Handovers are a key competency for collaborative robots and GPS could be a promising approach for this task, as it is data efficient and does not require prior knowledge of the robot and environment dynamics. However, existing uses of GPS did not consider important aspects of human-robot handovers, namely large spatial variations in reach locations, moving targets, and generalizing over mass changes induced by the object being handed over. In this work, we formulate the reach phase of handovers as an RL problem and then train a collaborative robot arm in a simulation environment. Our results indicate that GPS is limited in the spatial generalizability over variations in the target location, but that this issue can be mitigated with the addition of local controllers trained over target locations in the high error regions. Moreover, learned policies generalize well over a large range of end-effector masses. Moving targets can be reached with comparable errors using a global policy trained on static targets, but this results in inefficient, high-torque, trajectories. Training on moving targets improves trajectories, but results in worse worst-case performance. Initial results suggest that lower-dimensional state representations are beneficial for GPS performance in handovers.