Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Linearly Polarized Metal-only Beam Reconfigurable Magneto-electric Dipole Antenna

A novel beam reconfigurable magneto-electric dipole antenna is presented. The electric dipole is designed with two H-shaped patches and the E-shaped strip is designed as feeding structure to achieve wide impedance bandwidth. The upper ground etched with two symmetric slots is used to connect the magnetic dipole to reduce the profile. And the lower ground act as reflector to suppress the back radiation. Four binary switches are loaded on the stubs of the electric dipole. By controlling the state of each switch to ON or OFF, the antenna has the ability of beam reconfiguration. Finally, a set of prototype antenna with three configurations is simulated to verify the feasibility of design method. The -10 dB impedance bandwidth is 63 %. At 2.7 GHz, the beam directions of three states point to -22°, 16° and 33°, respectively in E-plane. The maximum gain at three states is 9.4 dBi.
New Developments in SiGe HBT Reliability for RF Through mmW Circuits

This paper reviews current progress in our understanding of the reliability of SiGe HBTs and the circuits built from them, including: 1) fundamental understanding of the multiple operative damage mechanisms, 2) the differences between DC and RF reliability, 3) the path to predictive reliability-aware compact models, and 4) the concept of Circuit-Safe Operating Area (C-SOA), which offers a better benchmark for understanding reliability-imposed constraints on circuits and systems.
Wearable Electrocardiogram Signal Monitoring and Analysis Based on Convolutional Neural Network

Wearable devices are increasingly popular for health monitoring via electrocardiograms (ECGs) as they can portably monitor heart conditions over a long time. However, so far there are no publicly available ECG data sets collected from wearable devices. Most ECG analysis algorithms target ECG data collected by hospital equipment. In the present study, we used the IREALCARE2.0 Flexible Cardiac Monitor Patch as the wearable device to collect ECG signals and formed ECG data sets. Wearable ECG data tended to contain more interference and be large in size. This article proposed a deep CNN approach, named time–spatial convolutional neural networks (TSCNNs), for the automatic classification and analysis of ECG signals from wearable devices. First, the original long-term ECG signals were divided into separate heartbeats and input into the TSCNN. Second, we applied convolution over time and spatial filtering for each heartbeat to extract abundant features. Finally, the cascaded small-scale kernel convolution was applied to improve classification performance and reduce the number of network parameters. To avoid overfitting, some regularized methods such as dropout and batch normalization were adopted. In the experiments, the method proposed in this letter is compared with other eight ECG classification algorithms. Our method attained the highest classification accuracy. The experimental results indicated that the proposed method can achieve better performance for wearable ECG data and can effectively monitor whether the wearer has an abnormal ECG.
Level of detail of the physical environment and its impact on propagation prediction for a 60-GHz fixed radio link

3D models that capture the characteristics of a physical environment in downtown Ottawa with three different levels of detail (LOD) have been developed. This paper investigates the impact of using different LODs of a downtown street canyon on multipath power predicted by ray tracing on a 60-GHz fixed radio link. The 60-GHz radios are equipped with high-gain directional antennas placed at street level below building rooftops. Results are produced and analyzed for two link configurations: with the transmitter and receiver placed on the same sidewalk of the street canyon and, with transmitter and receiver placed on opposite sidewalks.
Fake News Detection Using Content-Based Features and Machine Learning

The problem of fake news is a complex problem and is accompanied with social and economic ramifications. Targeted individuals and entities may lose trustworthiness, credibility and ultimately, suffer from reputation damages to their brand. Economically, an individual or brand may see fluctuations in revenue streams. In addition, the complex nature of the human language makes the problem of fake news a complex problem to solve for currently available computational remedies. The fight against the spread of fake news is a multi-disciplinary effort that will require research, collaboration and rapid development of tools and paradigms aimed at understanding and combating false information dissemination. This study explores fake news detection techniques using machine learning technology. Using a feature set which captures article structure, readability, and the similarity between the title and body, we show such features can deliver promising results. In the experiment, we select 6 machine learning algorithms, namely, AdaBoost as AB, Decision Tree as DT, K-Nearest Neighbour as KNN, Random Forest as RF, Support Vector Machine as SVM and XGBoost as XGB. To quantify a classifier’s performance, we use the confusion matrix model and other performance metrics. Given the structure of the experiment, we show the Support Vector Machine classifier provided the best overall results.
Influence of Online Meditational Aid on Emotional Health and Job Stress during Pandemic

Experts and youths for hundreds of years have practiced the art of mindfulness meditation. A reliable contemplation practice can help quiet your mind and do substantially more for your general wellbeing. Emotional intelligence or EQ is one’s ability to understand and manage emotions appropriately. Research indicates that EQ is a superior indicator of welfare & progress than IQ. Job stress occurs when the prerequisites of the employees do not coordinate with the abilities, assets, or necessities of the individuals. Research suggests that "thought decrease" or "mental quietness" may have explicit impacts applicable to work pressure and emotional welfare. Much of the population ventured into online mediums to continue their meditation practice due to its ease of accessibility and self-benefits specially during the pandemic. The two main aims of this paper were, 1) To study the difference between the experimental and control group on the dimensions of Emotional Intelligence. 2) To study the difference between the experimental and control group on the dimensions of Work Stress. An empirical- analytical approach was applied through an extensive review of existing literature and two predetermined questionnaires to study the Emotional Intelligence and Work stress level of individuals engaging in online meditation. For this study, an experimental research design was implemented for collection of data. Statistical Analysis through T-test was conducted on the data to understand the difference between two groups. Emotional health and stress are linked in their impact on an individual's well-being, which makes it crucial for future researchers to explore their interaction with mindfulness.
Commissioning of a Per-Element Beam Model for the LOFAR Radio Telescope

Until now the LOFAR (LOw Frequency ARray) radio telescope in the Netherlands used a simple beam model with identical element patterns to calibrate the array (assuming no mutual coupling). To improve the accuracy of the calibration, work has been set in motion to improve the beam model by full-wave electromagnetic simulations. This paper reports the initial commissioning results.
IC package related stress effects on the characteristics of ring oscillator circuits

The stress related shifts of transistors are measured by precise stress application with a newly designed in-situ four-point bending (4PB) system. A test board including a flip chip packaged test vehicle is loaded with uniaxial stress. The test vehicle contains dedicated ring oscillator circuits fabricated in the 22 nm FDSOI technology node, used to evaluate the effects of thermo-mechanical stress on the characteristics of CMOS devices. Finite element simulation provides insight into the originated stress values in the board, package, and active devices during mechanical loading. Considering the bending caused stress in the devices and the specific layout of the circuits, the directional frequency shifts of the circuits under stress are derived. These shifts are compared with a previous indentation study, which has been developed to induce very localized loads. The comparison aims for verification of the indentation approach to study directional stress related effects as well as very localized effects in chip stacks.
Comments on “Identity-Based Revocation From Subset Difference Methods Under Simple Assumptions”

An identity-based revocation (IBR) scheme is a useful one-to-many cryptographic message transmission method in which a message can be encrypted using receivers’ identities such as e-mail addresses as public keys and a trusted message sender who holds users’ private keys is not required. Recently, a construction method for an IBR scheme was presented with symmetric broadcast encryption (SBE) schemes called SD or LSD. In this article we clarify that the SBE schemes are completely different from the original subset difference (SD) scheme by Naor, Naor, and Lotspietch or the layered SD (LSD) by Halevy and Shamir. To be precise, we show that the IBR schemes built on top of the original SD or the original LSD scheme is insecure so that even revoked users can easily decrypt a ciphertext generated for a user group excluding the revoked users.
E-Governance: Contemporary Facets- A Comparative Study Between India and UAE

With the rapid rise in the use of internet and digitalization, governments from all over the world have taken necessary steps to E- governance, involving government process with IT. The author aims to establish the extensive change that the Government has undergone in the past decades and the four important pillars that lays as a platform for E- Governance. The author through this paper, is addressing the comparison between countries like India and UAE and its adaptation to E- governance. India’s attempt to adapting E- Governance through an initiative called Akshaya Project has gained recognition from various international organisations. UAE on the other hand has been seen incorporating modern technology and advancements in its administration and adapting E-Governance more successfully than India. The paper is structured into four parts. The first explains what exactly is a Good Governance. The second part elaborates on the shift towards E- Governance. The third part explains the adaptation of E-Governance in India and the challenges faced. Leading to the last part explains the start of E-governance in UAE. The author concludes on a hopeful note that digitalization and education shall make the acceptance of E- Governance smooth.
Automated Honeynet Deployment Strategy for Active Defense in Container-based Cloud

Lightweight virtualization represented by container technology has made continuous progress in the field of virtualization as an effective substitute for Virtual Machine(VM). However, its flexibility also makes the cloud system more dynamic and complex, which intensifies the unpredictable security problems. In this case, the defense mechanisms which use changeless implementation can hardly protect the attack surface in real time in the dynamic cloud system. To solve this problem, we present AHDS, an Automated Honeynet Deployment Strategy for active defense in the container-based cloud, to assess the variation of system structure and optimize the honeynet deployment strategy automatically. In the design of AHDS, we (1) establish an attack graph for formalizing various possible attack events in the container-based cloud system; (2) combine the concept of degree centrality with the attack graph for evaluating and optimizing the process to deploy honeynet; (3) design a framework to automatically generate, deploy and adjust honeynet deployment strategy. The results show that AHDS could reduce the success rate of attacks by 83% in container-based clouds and is flexible and scalable for large-scale implementation.
Real-Time Abnormal Insider Event Detection on Enterprise Resource Planning Systems via Predictive Auto-Regression Model

With the development of information and communication technology and the globalisation of enterprises, many companies are being operated through an electrical resource management system called Enterprise Resource Planning (ERP) system. An ERP system enables efficient and centralised resource management for enterprises. However, since many enterprise resources are being managed by the system, the threatening behaviour by an insider is one of the most significant risks in operating ERP systems. It is much stealthier and fatal compared with the threat from an outsider since it is considered as normal events that accessing the enterprise resource of insiders. Conventional insider threat detection methods have aimed to detect particular events manually defined by system administrators. Those approaches are not robust to the variation of event patterns, and they can not be used when the predefined cases are not given. In this paper, we present a real-time abnormal insider event detection method using the Predictive Auto-regression Model (PAM). Compared with the conventional approaches, the proposed method compiles a prediction model using normal events and identifies threat when the likelihood of prediction results is lower than a threshold. Experiments are conducted using a dataset including events defined as a sequence of ERP system logs. The logs are captured in a practical situation of an enterprise. The results demonstrate that the proposed method can successfully identify abnormal events of on ERP systems.
Spectral-Spatial Active Learning With Structure Density for Hyperspectral Classification

In this paper, a spectral-spatial active learning (AL) method is proposed based on an up-to-date unlabeled samples sampling strategy concentrated on the structure density supported by breaking ties. The proposed sampling criterion in AL is used for hyperspectral image classification, which involves several steps: First, superpixel segmentation algorithm is conducted on the HSI to cluster pixels with similar spectral-spatial signature into the same superpixel block. Then, density peak clustering technique is performed on the each superpixel block to obtain structure density of the pixels. Meanwhile, probability-based classifier is employed to achieve the probability distributions of pixel. Next, breaking ties (BT) score of each pixel can be calculated by exploiting the probabilities. Additionally, a fusion mechanism is introduced to select the unlabeled samples with representativeness and informativeness advantages by employing the BT-assisted structure density (SD sampling criterion) of each pixel. Finally, the samples with manual labeled class labels are put into the training set to retrain the classifier. Experimental results manifest that the proposed SD-based sampling criterion in active learning can significantly improve the classification accuracy in few labor costs. Thus, it has certain feasibility in practical application.
Stochastic and Deterministic Modeling Frameworks for Time Kinetics of Gate Insulator Traps During and After Hot Carrier Stress in MOSFETs

A stochastic Reaction-Diffusion-Drift (RDD) model framework is proposed for trap time kinetics under Hot Carrier Degradation (HCD) stress and post-stress conditions. Consistency of the 3-D stochastic RDD, 3-D TCAD incorporated deterministic RDD and an “equivalent” 1-D deterministic RDD frameworks is shown. Measured HCD kinetics is decoupled into contributions by pure HCD and Bias Temperature Instability (BTI). The pure HCD time kinetics during and after stress is modeled using the above frameworks. Lack of recovery for the pure HCD component after stress is explained.
A Parallel Data Stream Layer for Large Data Workloads on WANs

A variety of workloads on wide-area networks (WAN) can benefit from parallel data streams. Whether between public and private clouds or between high-performance computing centres, high bandwidth-delay-product (BDP) networks with even small amounts of packet loss (e.g., due to congestion) can suffer reduced TCP/IP throughput. Therefore, we design, implement, and evaluate the open-source Parallel Data Streams (PDS) user-level tool. PDS makes trade-offs (e.g., TCP fairness) for specific workloads, when performance is the key goal. Unlike the well-known GridFTP, PDS also supports tools such as rsync, git, Virtual Network Computing (VNC), and the Network File System (NFS). We also quantify and contribute a performance evaluation of PDS, using a combination of emulated and real WANs. We establish that PDS achieves comparable performance to GridFTP for file transfer, but additional functionality via other tools. For example, PDS can transfer a 14 GB file on a WAN between Alberta and Quebec (maximum 1 Gbps; over 3,100 km) at 861 Mbps, using rsync and 8 parallel, cleartext, TCP streams. In comparison, rsync over a single SSH stream achieves 274 Mbps.
5G SLAM with Low-complexity Channel Estimation

5G millimeter-wave signals are beneficial for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment. Channel estimators can exploit received signals to estimate multipath components in terms of delays and angles, which can be used in localization and mapping. Thus, a good channel estimator is essential for 5G SLAM. This paper presents a novel low-complexity multidimensional ESPRIT-based channel estimator and applies it to a 5G SLAM framework. Simulation results demonstrate that the proposed channel estimator can accurately estimate channel information with low computational cost, with limited impact on mapping performance, compared to a tensor-ESPRIT benchmark.
Dual-Polarized Filtering Antenna for mm-Wave 5G Base Station Antenna Array

This paper presents a dual-polarized filtering antenna designed for a 5G mm-Wave base station phased array. The out-of-band radiation of a stacked patch antenna is suppressed by embedding filtering structures along with radiating patches and feed network. Four radiation nulls can be tuned by introducing a combination of open-loop and hairpin resonators at appropriate locations, and a bandpass filtering response can be achieved. The antenna design principles and simulated performance are discussed. The antenna operates in n257 and n258 mm-Wave bands, demonstrating -10 dB impedance bandwidth at 24.25-29.5 GHz. The realized gain remains stable between 5 and 6 dBi at all the operating frequencies. The isolation between the ports and cross-polar discrimination remain better than 20 dB in all the covered frequency range.
mmWave Channel Estimation via Compressive Covariance Estimation: Role of Sparsity and Intra-Vector Correlation

In this work, we address the problem of multiple-input multiple-output mmWave channel estimation in a hybrid analog-digital architecture, by exploiting both the underlying spatial sparsity as well as the spatial correlation in the channel. We accomplish this via compressive covariance estimation, where we estimate the channel covariance matrix from noisy low dimensional projections of the channel obtained in the pilot transmission phase. We use the estimated covariance matrix as a plug-in to the linear minimum mean square estimator to obtain the channel estimate. We present a new Gaussian prior model, inspired by sparse Bayesian learning (SBL), which incorporates parameters to capture the channel correlation in addition to sparsity. Based on this prior, we develop the Corr-SBL algorithm, which uses an expectation maximization procedure to learn the parameters of the prior and update the posterior channel estimates. A closed form solution is obtained for the maximization step based on fixed-point iterations. To facilitate practical implementation, an online version of the algorithm is developed which significantly reduces the latency at a marginal loss in performance. The efficacy of the prior model is studied by analyzing the normalized mean squared error in the channel estimate. Our results show that, when compared to a genie-aided estimator and other existing sparse recovery algorithms, exploiting both sparsity and correlation results in significant performance gains, even under imperfect covariance estimates obtained using a limited number of samples.
Vertical stack reliability of GaN-on-Si buffers for low-voltage applications

In this paper the reliability of the vertical GaN-on-Si stack for lateral p-GaN HEMTs dedicated to low-voltage applications is discussed in detail by comparing wafers with different buffer thicknesses and growth condition of the AlN nucleation layer. The vertical robustness and the time-dependent vertical breakdown will be investigated in detail, demonstrating that the buffers with reduced thickness are suitable for 100 V applications. Moreover, the voltage drop on the different layers of the vertical stack will be extracted at the breakdown, and a model able to explain the degradation of the vertical stack will be proposed.
A optimized YOLO method for object detection

With the continuous breakthrough of object detection, there are many achievements in the combination field of computer science and mechanical automation. Especially in manufacturing industry, the object detection has played a great role in the weld inspection link during the production of vehicle wheels. The algorithm is used to align the weld on the wheel hub with the air sensor by identifying welds and rotate wheel hub in order to achieve the purpose of automatic inspection. The application of this technology can greatly Improve detection efficiency and accuracy, getting rid of the inconvenience caused by manual detection. There are few applications of object detection for wheel weld quality detection at present. The aim of this paper is to use improved YOLOv3 detector for vehicle wheel weld detecting applications to meet the needs of the industry. YOLOv3 as a one-stage deep learning-based approach, solves the shortcomings of traditional machine learning-based approach such as high time complexity and many redundant windows, which seriously affect the speed and performance of subsequent feature extraction and classification, since YOLO is one of the ends to end object detection algorithms. In this paper, we modify the original yolov3 model according to the actual situation of vehicle wheel weld. On the basis of the original model structure and layers, the paper uses Distance-IoU(DIoU) loss to improve the loss function of yolov3 and Non-maximum suppression using distance-IoU(DIoU-NMS) to eliminate the redundant candidate bounding boxes, which further accelerates the convergence speed of loss function and improves the accuracy of object detection(6.91-point higher than base model in AP50 and 3.61%-point higher in AP75).
A Parallel-Plate Lens for Mechanical Wide-Angle Beam Scanning : (Invited Paper)

This paper presents a parallel-plate waveguide lens for wide-angle mechanical beam steering. A previously developed ray-tracing procedure is used for designing the quasi-optical system. The feed system is based on gap waveguide technology enabling a simple and rapid mechanical actuation. Validation of the proposed concept is given by numerical results for a Ka-band lens design. A high scanning performance over an angular range of ± 50° with a worst-case scan loss of about 3 dB is achieved. The simulated return loss is greater than 15dB in the 27.5-31 GHz band. The proposed lens beamformer is an attractive ground-segment solution for future Satcom applications.