Miracle-dz/UniFAR-FTUs huggingface dataset:
A Compact Helix Antenna With Wide Axial Ratio Bandwidth Using a Spiral Microstrip Coupling Feedline for GNSS Applications

A compact quadrangular prism-shaped slot helix antenna with wide axial ratio (AR) bandwidth using a spiral microstrip coupling feedline is presented. The proposed antenna is constructed with FR4 substrate copper-clad plate with four slots symmetrically etched on the substrate exterior surface and a spiral microstrip coupling feedline printed on the substrate inner surface. The electric field and equivalent magnetic current excited by the spiral microstrip feedline in slots are analyzed. The wide AR bandwidth is obtained. The proposed antenna has a small dimension of 0.156<italic>λ</italic><sub>0</sub> × 0.156 <italic>λ</italic><sub>0</sub> × 0.182 <italic>λ</italic><sub>0</sub> (<italic>λ</italic><sub>0</sub> is the free-space wavelength at the center frequency). Its measured –10 dB |<italic>S</italic><sub>11</sub>| bandwidths and 3 dB AR bandwidths are 1525–1662 and 1497–1630 MHz, respectively. It generates right-hand circular polarization (RHCP) without a complicated feeding network. It promises a good use in Global Navigation Satellite Systems.
Microwave Acoustic Devices: Recent Advances and Outlook

This paper presents a short review of the microwave acoustics area, where exciting material innovations and performance advancements have been made in the past decade. The ever-growing demand for more sophisticated passive signal processing functions on-chip has fueled these developments. As a result, microwave acoustic devices have maintained performance leadership in mobile applications. By evaluating three fundamental parameters, namely electromechanical coupling (k2), quality factors, and frequency scalability, of microwave acoustics, this paper aims to, extensively but not exhaustively, capture the rationales behind approaches achieving higher performance microsystems. Outlooks for different material systems and addressing their underlying challenges are also offered in hopes of establishing a balanced roadmap for future microwave acoustics development.
A Wideband Dual-Polarized Antenna for Millimeter-Wave Applications

A wideband dual-polarized magneto-electric (ME) dipole for millimeter waveband applications is presented. The ME dipole consists of four shorted patches, which are fed by two pairs of T-shaped probes orthogonally oriented on the same layer. The feed lines are connected with the T-shaped probes through metallic vias. Simulated overlapped impedance bandwidth of 50% with return loss larger than 10 dB, gain up to 9.4 dBi, and isolation higher than 17.8 dB between dual polarizations are achieved. By combining the antenna elements with single-layered feed networks, a $2 \times 2$ array is designed, fabricated, and measured. The overlapped working bandwidth is 50%, with gain up 14.8 dBi. With the advantages of wide bandwidth, single-layered radiation structure, and promising radiation pattern, the proposed antenna with dual polarizations would be attractive for millimeter-wave antenna in package (AiP) applications.
Adaptive Machine learning: A Framework for Active Malware Detection

Applications of Machine Learning (ML) algorithms in cybersecurity provide significant performance enhancement over traditional rule-based algorithms. These intelligent cyber-security solutions demand careful integration of the learning algorithms to develop a significant cyber incident detection system to formulate security analysts’ industrial level. The development of advanced malware programs poses a critical threat to cybersecurity systems. Hence, an efficient, robust, and scalable malware recognition module is essential for every cybersecurity product. Conventional Signature-based methods struggle in terms of robustness and effectiveness during malware detection, specifically in the case of zero-day and polymorphic viruses attacks. In this paper, we design an adaptive Machine Learning based active malware detection framework which provides a cybersecurity solution against phishing attacks. The proposed framework utilize ML algorithms in a multilayered feed-forwarding approach to successfully detect the malware by examining the static features of the web pages. The proposed framework successfully extracts the features from the web pages and performs a successful detection process for the phishing attack. In the multilayered feed-forwarding framework, the first layer utilizes Random Forest (RF), Support Vector Machine (SVN), and K-Nearest Neighbor (K-NN) classifiers to build a model for detecting malware from the real-time input. The output of the first layer passes to the Ensemble Voting (EV) algorithm, which accumulates earlier classifiers’ performance. At the third layer, adaptive frameworks investigate second layer input data and formulate the phishing detection model. We analyze the proposed framework’s performance on three different phishing datasets and validate the higher accuracy rate.
Decoding trajectories of imagined hand movement using electrocorticograms for brain–machine interface

Objective. Reaching hand movement is an important motor skill actively examined in the brain–computer interface (BCI). Among the various components of movement analyzed is the hand’s trajectory, which describes the hand’s continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement. Approach. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action. Main results. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson’s correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively. Significance. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.
A CNN Based Method to Solve Class Imbalance Problem in SAR Image Ship Target Recognition

Synthetic Aperture Radar (SAR) recognition algorithms have made great progress with the use of convolutional neural networks (CNNs). However, the imbalanced distribution of ship targets causes CNNs trained by conventional methods to fail to recognize rare ship types in the marine target recognition field. Traditional resampling methods have disadvantages of overfitting and low recognition accuracy, perform poorly in SAR images. To address this problem, this paper proposes a dynamic weighted sampling and soft threshold moving combined method which can be used in most current CNN based SAR recognition algorithms. Specifically, dynamic weighted sampling changes training data distribution according to the validation set performance, and soft threshold moving adjusts output decision thresholds of trained CNNs. The test results on the public dataset OpenSARShip show that our method can effectively identify rare ship types while obtaining a higher recognition accuracy than traditional resampling methods.
Research on Human Motion Monitoring Method Based on Multi-Joint Constraint Filter Model

In recent years, with the increasing application of human motion monitoring technology in virtual reality, medical health, human-computer interaction, and other fields, higher requirements have been put forward for measurement accuracy. Aiming at the problem of large accumulative measurement error and multi-joint constraint in the traditional inertial measurement system, a multi-joint constrained filter model human motion monitoring method based on data fusion is proposed. Firstly, an accelerometer and magnetometer are used to construct the initial attitude angle calculation model of the human body at rest. Secondly, combined with gyroscope, accelerometer and magnetometer, the attitude optimal estimation model based on Extended Kalman Filter (EKF) algorithm for human body motion was established. Then, a global coordinate system of human multi-joints is established, and the joint constraint is introduced into the attitude optimal estimation model by using the estimation projection method, and the multi-joint constraint filtering model of human motion monitoring is constructed. Finally, a multi-inertial measurement unit (MIMU) human motion monitoring system was built, and the simulation and experimental analysis were carried out. The experimental results show that the maximum angular measurement error is about 1° after filtering with the motion monitoring method proposed in this paper. Compared with the existing algorithm, the accuracy of the three angle measurements is improved by 38.2%, 39.3% and 67.4%, respectively. Based on this, the human motion monitoring system designed can effectively monitor human motion.
IoT-Enabled Smart Energy Grid: Applications and Challenges

The Internet of Things (IoT) is a rapidly emerging field of technologies that delivers numerous cutting-edge solutions in various domains including the critical infrastructures. Thanks to the IoT, the conventional power system network can be transformed into an effective and smarter energy grid. In this article, we review the architecture and functionalities of IoT-enabled smart energy grid systems. Specifically, we focus on different IoT technologies including sensing, communication, computing technologies, and their standards in relation to smart energy grid. This article also presents a comprehensive overview of existing studies on IoT applications to the smart grid system. Based on recent surveys and literature, we observe that the security vulnerabilities related to IoT technologies have been attributed as one of the major concerns of IoT-enabled energy systems. Therefore, we review the existing threat and attack models for IoT-enabled energy systems and summarize mitigation techniques for those security vulnerabilities. Finally, we highlight how advanced technologies (e.g., blockchain, machine learning, and artificial intelligence) can complement IoT-enabled energy systems to be more resilient and secure and overcome the existing difficulties so that they become more effective, robust, and reliable in operation. Precisely, this article will help understand the framework for IoT-enabled smart energy system, associated security vulnerabilities, and prospects of advanced technologies to improve the effectiveness of smart energy systems.
A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR

This article presents a new method for effective detection of ac series arc fault (AF) (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and nonarcing current due to their similarity. Different from the traditional method, in the proposed method, the differential magnetic flux is coupled to obtain high-frequency signals by putting the live line and the neutral line through the current transformer, which can effectively solve the problem of SAF features disappearing in the trunk-line current. However, similar to the traditional method, the effectiveness of the proposed coupling method could also be compromised when being used in cases with dimmer load and load starting process. This is found to be caused by the presence of high-amplitude pulse phenomenon in the nonarcing signals in these scenarios, which are incorrectly detected as arcing signals in other loads. To address this issue, a short-observation-window singular value decomposition and reconstruction algorithm (SOW-SVDR) is used to enhance the capability to identify SAFs by the coupling method. The proposed method has been implemented and validated according to the UL1699 standard with different types of loads connected to the system and also tested under their starting processes. The experimental results show that the proposed approach is more effective in detecting AFs compared with existing methods.
NUPFA: A Novel Nonuniform Power Formation Algorithm for BLE Mesh Networks

This study presents a novel nonuniform power formation algorithm (NUPFA) for Bluetooth low-energy (BLE) networks. With two stages, NUPFA constructs the scatternet in a distributed manner. In the first stage, the master and slave nodes discover each other using low-level power to form an initial scatternet. In the second stage, each master alternatively scans or advertises using normal power to interconnect dual links among any two piconets until the whole scatternet is constructed. Using nonuniform power configuration, an energy-efficient scatternet is generated, since packet transmission power can be reduced in each piconet to minimize overall power consumption. With additional scatternet links, more routing paths can be utilized to reduce the path length and enhance network efficiency. simulation results show that compared with the existing cluster-based on-demand routing protocol (CORP) and Bluetooth Mesh schemes, the NUPFA protocol achieves superior energy efficiency for packet transmission. Also, the dual link features can effectively reduce packet latency, thereby improving the overall performance of BLE mesh networks.
A Fault-tolerant Method of SRAM FPGA Based on Processor Scrubbing

In order to improve the reliability of FPGA in space applications, a method of single event upset detection and scrubbing based on internal processor is proposed for Xilinx Zynq device. We propose a method to reduce the sensitive configuration bits and consequently the global scrubbing time based on Xilinx Essential Bits technology. Then the impact on reducing sensitive frames by placing different layout constraints is analyzed. The physical address of one single configuration frame and its corresponding configuration data are obtained based on the configuration memory structure. Simultaneously, Frame_ECC primitive and global CRC circuit is added to the design. Finally, the feasibility of our system is verified by the fault injection, and the time performance is evaluated by Mean Time to Detect (MTTD). The results show that the system can effectively shorten the readback and scrubbing time, single-bit upset and multi-bit upsets can be detected and repaired. Thus, the work is of great significance for improving the reliability of aerospace electronic systems.
Affordable Time-Domain Reflectometry System for Rapid Food Analysis

The development of an inexpensive instrumental chain based on time-domain reflectometry is presented. The attention paid to the choice of the components allowed purchase costs reduction compared to the instruments available on commerce. The chain is composed by a hand-made 30-mm needle probe, a square wave fast rise time pulses generator on board, and a digital sampling USB oscilloscope with bandwidth of 11 GHz. Stability measurements and sodium chloride, sucrose, and cow milk were conducted to assess device repeatability and evaluate the predictive potential of estimation for some food parameters. The traditional geometrical-mathematical approach was replaced by multivariate investigation of the time-domain signal. Good results in terms of coefficient of determination up to 0.983 were obtained. The results disclosed show how a cheap and easy-to-use instrumental chain can be very promising for attributes determinations of liquid materials, such as foodstuff.
Navigation in GPS Spoofed Environment Using M-Best Positioning Algorithm and Data Association

Intentionally misguiding a global positioning system (GPS) receiver has become a potential threat to almost all civilian GPS receivers in recent years. GPS spoofing is among the types of intentional interference, in which a spoofing device transmits spoofed signals towards the GPS receiver to alter the GPS positioning information. This paper presents a robust positioning algorithm, followed by a track filter, to mitigate the effects of spoofing. It is proposed to accept the authentic GPS signals and spoofed GPS signals into the positioning algorithm and perform the robust positioning with all possible combinations of authentic and spoofed pseudorange measurements. The pseudorange positioning algorithm is accomplished using an iterative least squares (ILS). Further, to efficiently represent the robust algorithm, the M-best position algorithm is proposed, in which a likelihood-based cost function optimizes the positions and only provides M-best positions at a given epoch. However, during robust positioning, the positions evolved due to spoofed pseudorange measurements are removed to overcome GPS spoofing. In order to remove the fake positions being evolved owing to wrong measurement associations in the ILS, a gating technique is applied within the Kalman filter (KF) framework. The navigation filter is a three-dimensional KF with a constant velocity (CV) model, all the position estimates evolved at a specific epoch are observations. Besides, to enhance this technique’s performance, the track to position association is performed by using two data association algorithms: nearest neighbor (NN) and probabilistic data association (PDA). Simulations are carried out for GPS receiver positioning by injecting different combinations of spoofed signals into the receiver. The proposed algorithm’s efficiency is given by a success rate metric (defined as the navigation track to follow the true trajectory rather than spoofing trajectory) and position root mean square error (PRMSE).
A Novel SSVEP-based Word Speller Based on Sliding Multi-Window Strategy

Speller has been the strong illustration of steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) for being an efficient communication interface. However, the main limitation of the SSVEP speller is the low communication rates caused by extra input time for gaze shifting and waiting. In this study, a novel spelling method using the word as an input unit is proposed, which saves the waiting time between two consecutive characters. SSVEP features are extracted from continuous EEG signals containing different frequencies using filter bank canonical correlation analysis (FBCCA) with sliding multi-window strategy. Both offline and online experiments have been conducted with ten participants by a 40-character speller. The average accuracy and information transfer rate (ITR) of the proposed word speller reached $90.72\pm 7.27\%$ and $220.40\pm 22.68$ bits/min respectively at a 1.2 s time window, higher than the traditional character-based speller with $87.52\pm 6.19\%$ accuracy and $155.60 \pm 16.01\ \text{bits}/\text{min}$ ITR. The improvements demonstrated the efficiency of the sliding multi-window strategy for an SSVEP-based word speller.
A K-Band Variable-Gain Phase Shifter Based on Gilbert-Cell Vector Synthesizer With RC–RL Poly-Phase Filter

A ${K}$ -band variable-gain phase shifter using a Gilbert-cell vector synthesizer with a current digital-to-analog converter (DAC) and an RC–RL poly-phase filter (PPF) is presented, which is fabricated with a 65-nm RF CMOS process. It controls both gain and phase with low phase and gain variations as well as a low insertion loss. The Gilbert cell synthesizes a vector by summing in-phase (I) and quadrature (Q) phase signals of which the amplitudes are decided by the tail currents of the summing cells, respectively. The tail currents are given by current DACs, which are particularly designed to make the synthesizer have a constant output impedance in all phase and gain states. Because the gain and phase control resolutions depend on those of the current DACs, the size and insertion loss of the variable-gain phase shifter are not dependent on the control resolutions. The total root-mean-square (rms) phase and gain errors are measured to be 0.488° and 0.098 dB for 4096 states of the 7-bit 360° phase and 5-bit 17.8-dB gain controls, respectively, which are calibrated with extra 2-phase and 1-gain control bits. It shows −3.5 dB maximum gain including ON-chip balun losses with 6.6-mW dc power consumption.
The Short-time Traffic Flow Prediction at Ramp Junction Based on Wavelet Neural Network

In order to accurately analyze the impact of ramp traffic flow on main road traffic flow, it is necessary to predict short-time traffic flow at ramp junction. Based on the comprehensive analysis of the characteristics of short-time distribution of traffic flow at typical ramp junction, 15 minutes was selected as the traffic flow sampling interval, the traffic flow time series data chain is formed. And the structure of wavelet neural network is optimized and trained by means of the gradient method used to modify the network weights and wavelet basis functions. The short-time traffic flow prediction algorithm with high fitting accuracy and no repeated correction is formed. The prediction training, fitting and error correction of the model are carried out by using the 300 traffic flow time series obtained from road monitoring. The real-time prediction analysis shows that the prediction algorithm can improve the matching accuracy of short-time traffic flow.
Review on Wideband and Compact CTS Arrays at Millimeter Waves

In this paper, three state-of-the-art parallel-fed continuous transverse stub (CTS) arrays are presented. They operate at various frequency bands ranging from 20 GHz up to 86 GHz. The multiple capabilities of this architecture, such as side-lobe level reduction, active beam switching, and wideband operation with an ultra-low-profile, are here reviewed. Moreover, the suitability of the antenna concept for multiple fabrication technologies is demonstrated. The performance and key design solutions of three prototypes manufactured using milling techniques, low-temperature co-fired ceramic and printed circuit board technology are showcased.
Gradual Shrinkage of Feature Space using ANOVA for WiFi-based Indoor Localization

It is high time researchers realized the potential of statistical inference in setting expectations from features in prioritizing them. In this age of fanatic developments in artificial intelligence, no model would live up to satisfaction in case the features are dismal and insignificant. So, an insight into the significance of features may come to aid prior to delving into arduous hyperparameter tuning. In this paper, we explore the problem of indoor localization and navigation—feeding to an eternal inquisitiveness of humans about where they are at some unknown venue—showing how inferential ANOVA (Analysis of Variance) can shed off noisy features. We utilize ANOVA for double-thresholding, with the first phase eliminating features with p-values >0.05 and the second round discarding features with F-ratios <70, across six indoor locations. This scaled down the feature-count from an initial 127 to an intermediate 125 to a final 50—making no compromise in accuracy or F1-score. We cross-validate the shrunk feature-set using kNN, assuming this will render more reliable metrics than neural networks for the purpose. The approach has shown extendibility to multiple classes and has enabled numerical quantification of the impact of each continuous feature—empowering the decision whether to retain the feature, to the alleviation of the curse of dimensionality.