Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of a Scalable Distributed DNS System

Based on blockchain technology and interstellar file systems, this paper proposes a domain name system (DNS), in which the domain specific names of important institutions and organizations are permanently and safelyidentified in the super account management system in the form of transaction, so as to realize the naming on the blockchain, while the domain names that need to be expanded by institutions, teams and individuals adopt the distributed storage under the chain to form a DNS system architecture with the combination of online blockchain and offline blockchains. This paper analyzes the specific deployment of the architecture and the implementation of domain name management in detail. The domain naming management for organizations on the chain is based on a fabric alliance chain, while the extended domain naming data management under the chain uses distributed file storage technology to store, update and query the extended domain names. At the same time, the hyperledger on the chain is used to establish the trust chain of signature verification for the extended domain names and the extended domain name data files. Finally, it ensures the scalability of the whole domain name system and the efficiency and security of domain name resolution dynamically.
Sulfur-Doped TiO2 Nanocrystalline Photoanodes for Dye-Sensitized Solar Cells by Hydrothermal

Sulfur-doped TiO2 photoanode was prepared by hydrothermal method, and then characterized and analyzed through XRD, UV-vis, and XPS. The results showed that: Sulfur-doped TiO2 doped by sulfur was anatase; Sulfur-doped TiO2 atoms improved visible light absorption ability of TiO2; The performance of DSSCs doped by sulfur improved markedly, in which 0.770 g thiourea sample performed the best. Its open-circuit voltage was 0.72 V, short-circuit current density was 16 mA·cm −2 , and photoelectric conversion efficiency reached 14.82% high. The improvement of efficiency is resulted from the Sulfur-doped which leads to the following effects: more electrons from dye are injected into the TiO2 nanometer crystal, the electron transfer rate is enhanced, therefore short circuit current density of the battery is increased, finally the efficiency of the battery is improved.
Tensor Topic Models with Graphs and Applications on Individualized Travel Patterns

Individualized passenger travel pattern is of significant research value since the abundant information from individual trajectory data could help discover the useful insights about the multi-clustering of origin, destination, time, etc., and the passenger cluster. However, this task is rather challenging, given the data is high-dimensional, with complex spatiotemporal structure, and exhibits sparse patterns. Moreover, individual travel patterns are also affected by external information, such as the distances and functions of locations and points of interest, which are ignored in most current works. To tackle these challenges, we proposed two novel frameworks based on topic models with the external information incorporated as graphs: Trips and passengers are formulated as tensor words and tensor documents to preserve data nature; To learn multiclustering, we proposed a graph-regularized tensor Latent Dirichlet Allocation model, with graph structure formulated as Laplacian penalty; To learn passenger clustering, we proposed a graph-based tensor Dirich-let Multinomial Mixture model with graph Laplacian penalty and l1-norm penalty for cluster amount autodetermination.
optimized Rectangular Planar Coil Design for Wireless Power Transfer with Free-Positioning

This paper introduces a new method for optimizing the efficiency of two planar coils for a wireless power transfer system, assuring a specific pre-defined efficiency over the entire given charging surface. The referred efficiency is calculated from the transmitter’s source to the receiver’s load. This study is based on rectangular shaped coils. The electrical parameters, coupling and efficiency of the system are calculated and optimized as function of their physical dimensions. Thus, taking those parameters into account a useful design tool is proposed here. One of many possible designs is presented as example, which proves by theoretical and simulation results that the receiver might be free-positioned over the surface of charging without losing efficiency. Results for a 13.56 MHz system show that more than 82 % of efficiency (load to transmitter coil) can be achieved for a 4 cm x4 cm receiver moving over a 12 cm x8 cm surface.
Dispenser Printed Flexible Rectenna for Dual-ISM Band High-Efficiency Supercapacitor Charging

This paper presents a dual-band sub-1 GHz rectenna for near and far-field powering of Internet of Things (IoT) nodes. The rectifier is based on a coplanar waveguide (CPW) voltage doubler with inductive matching, achieving over 80% power conversion efficiency (PCE) at 10 dBm and 915 MHz. The rectifier is designed to directly charge a 6.8 mF supercapacitor with no DC power management circuitry or load tracking, achieving the highest reported average charging efficiency of 47.2% and 33.3%, at 433 and 915 MHz respectively. From 6 dBm input power, a 1.5-2.8 mA dummy load can be RF-powered with over 30% duty-cycle at 915 MHz. A dual-band single-layer antenna is designed and fabricated using dispenser printing on a flexible polyimide substrate. The antenna maintains over 2.1 dBi gain at 915 MHz with omnidirectional patterns, while occupying under 10 $\times$ 10 cm. The integrated rectenna is evaluated in two real-world applications: energy harvesting from a 915 MHz transmitter showing a 49% wireless charging efficiency for a $7.4 \mu W/cm^{2}$ incident power density; and ambient energy harvesting generating 47 mJ in 5 seconds at 3 to 5 cm from a handheld two-way radio.
Lung Field Segmentation of X-ray Images by Normalized Gradient Gaussian Filter and Snake Segmentation

Scientists have considered the programmed segmentation of lung areas as a fundamental pre-processing undertaking when examining pulmonary radiographs (CXRs) obviously. In this paper, a method for lung field segmentation is proposed. This is performed on MATLAB image processing coding. It improves the speed of processing and detects abnormality in the lung area. The method used is Normalized Gradient Gaussian Filter and Snake segmentation is combined to improve the performance of edge detector
ArtMatch: Classifying Famous Paintings and Matching Them with Children’s Artwork

This paper represents the progress of an ongoing effort to develop a mobile app called ArtMatch, which will help kids develop a passion for art. The proposed app will deepen children's appreciation for famous pieces of artwork while enhancing their creativity. By taking an image of artwork created by the user, ArtMatch will show the most similar painting done by a famous artist, using AI and deep learning technology. The app works through a program that clusters images based on their design and style. Throughout this process, the user will see the effectiveness of his/her painting through their match with well-known paintings. The program’s present version can generate accurate matches for several works done by young children.
An Active Voltage Stabilizer for a DC Microgrid System

This paper analyzes the low-frequency stability challenges that exist in a complex DC microgrid (MG) system. The converters that belong to a DC MG are categorized into different groups based on their control approach. The small-signal model of the DC MG is presented, and the conditions for system stability are derived. In some DC MG applications, there is the possibility of installing off-the-shelf converters with little flexibility and access for controller auto-tuning. To tackle this, a hardware-based active voltage stabilizer solution is proposed to stabilize the DC MG. The active stabilizer’s functionality, based on an isolated bidirectional DC-DC converter, is elucidated, and a suitable control strategy is proposed. The active stabilizer and its associated control configuration involve only local voltage sensing and are non-intrusive. A dual active bridge (DAB) converter-based active stabilizer is implemented, and hardware-based steady-state and transient experimental results from a DC MG test-bench are provided to validate the functionality and effectiveness of the proposed active stabilizer.
A Review of Arrhythmia Classification with Artificial Intelligence Techniques: Deep vs Machine Learning

Cardiovascular diseases like arrhythmia are a significant health concern worldwide, affecting both elderly and young population due to lifestlye changes. Early diagnosis of cardiac arrhythmia using Electrocardiogram (ECG) by trained cardiologists is vital to prevent heart ailments and save lives. With the growth of wearable and standard ECG monitoring devices and a dearth of qualified cardiologists required to analyse the vast amounts of data collected, automated arrhythmia detection by Machine Learning (ML) and Deep Learning (DL) techniques have become very popular in recent years. In this study, we have reviewed the literature and described standard ML and DL studies in ECG arrhythmia classification. While ML techniques do demonstrate very good metrics, ML classifiers like SVM, knearest-neighbours, Decision Trees, etc. need preprocessing and hand-crafted feature extraction. DL methods which use networks like Convolutional Neural Networks (CNN), Long-Short-Term-Memory (LSTM) do not need any feature extraction as they automatically learn the features by themselves. Recent studies in DL have demonstrated very high performance metrics without the need for feature extraction. While some DL techniques do need noise filtering and determination of other features like the QRS complex, many of them can work with raw ECG signals and hence are ideally suited over their ML counterparts for real time ECG classification. DL networks can also be used as feature extractors and combined with ML classifiers. We thus conclude that state-of-the-art DL methods offer inherent advantages and flexibility over ML methods for automated arrhythmia classification. This review aggregates the niche features of leading ML and DL studies in this field which interested researchers can benefit from.
400-W UAV/Drone Inductive Charging System Prototyped for Overhead Power Transmission Line Patrol

This paper reports 400-W 85-kHz band inductive charging system prototype for UAV or drone to be employed for overhead power transmission line patrol. For patrol of the long overhead power transmission line network, the consecutive charging opportunities which will be provided by multiple rapid wireless charging ports are desired. The rapid inductive charging system with frustum-shape wireless charging port and lightweight on-board power receive equipment is prototyped to serve such opportunity charging. The successful test of sequential operation of landing on the wireless charging port, 400-W or more wireless charging of lithium ion battery, taking off, and photo-shooting of overhead power transmission lines between actual high-rise steel towers by high-precision camera, show a great potential of the proposed inductive charging system for opportunity charging of long range patrol.
Computational Strategies Improvement For The Unstructured Inductive PEEC Method

Computational strategies improvements for the inductive unstructured PEEC method are presented in order to address efficiently low frequencies electromagnetic problems. Couplings between volume, surface and line regions have been developed to reduce the number of degrees of freedom and thus the computational cost. Good accuracy on results is ensured thanks to the use of an adaptive Gauss integration procedure for the computation of near interactions. Multi-threaded AMLFMM matrix compression algorithm is used to speed-up far interactions computation. External circuit components can also be coupled to the meshed conductive regions.
Satellite On-Orbit Anomaly Detection Method Based on a Dynamic Threshold and Causality Pruning

It is difficult for existing deep learning-based satellite on-orbit anomaly detection methods to define the residual-based detection threshold and identify false anomalies. To solve the above problems, this paper proposes both a detection threshold determination and dynamic correction method and a causality-based false anomaly identification and pruning method. We use the GRU (Gated Recurrent Unit) to model and predict the telemetry parameters to obtain the residual vector; determine and dynamically correct the threshold according to the prescribed false positive rate; propose an improved multivariate transfer entropy method to identify the causal relationships between the telemetry parameters; and, based on the causality, determine whether the detected parameter anomalies are false. Experiments show that the precision, recall, and F1-score of the method proposed in this paper are superior to the current typical method, and the false positive rate is significantly reduced, demonstrating the effectiveness of the proposed method.
High Gain and Stable Class AB-AB Miller Op-Amp for Large Capacitive Loads Ranging From 15pF

In this concise, highly efficient class AB-AB Miller op-amp circuit is introduced. This paper has planned to provide a powerful class AB-AB Miller operational amplifier with high Trans-conductance, open loop dc gain and unity gain frequency with insignificant increase in static power utilizations by using linear common mode feedback (LCMFB) resistor. By the utilization of Miller and phase-lead compensation network, high stability for large values of load capacitances (CL) achieved. The design of class AB-AB op-amp carried out in 180nm CMOS technology shows 109-dB open loop gain, 44-KHz bandwidth, 61-degree phase margin with $50-\mu\mathrm{w}$ power consumption.
Compact Dual Band Ring Coupler Using Miniaturized Metamaterial Left-Handed Impedance Inverters

A compact 3-dB dual band ring coupler is designed, fabricated and tested. The coupler consists of three identical miniaturized Left-Handed (LH) impedance inverter transmission lines (TLs) and a dual one. The general principle of using LH TLs’ duality to reduce the size of a dual band ring coupler is explained in detail, showing the equivalent lumped element circuits and the designing relations. A numerical example is considered for the duality analysis and the results are used to obtain a size reduction for both the coupler’s dimensions and the number of utilized components. Other arbitrary coupling levels (1 dB and 5 dB) and frequency ratios are investigated and the results of simulations show very good results, proving the validity of the general principle of using LH transmission line’s duality. The 3-dB coupler is implemented in microstrip technology and using high-quality lumped components. It works at two arbitrary frequencies, 1.1 GHz and 2.5 GHz, respectively with similar performances. The overall dimension of the proposed coupler is $0.07\lambda \times 0.07\lambda $ for a wavelength, $\lambda $ computed at 1.1 GHz. The measured results are in good agreement with the predicted ones for both frequencies, having the return loss and isolation loss better than 33 dB, whereas keeping the insertion and coupling losses at around 3.5 dB in both frequency bands. The phase imbalance at the output ports is less than 1.5°, when the difference and sum ports are excited.
DPTL+: Efficient Parallel Triangle Listing on Batch-Dynamic Graphs

Triangle listing is an important topic in many practical applications. We have observed that this problem has not yet been studied systematically in the context of batch-dynamic graphs. In this paper, we aim to fill this gap by developing novel and efficient parallel solutions. Specifically, given a graph G and a batch-update of edges B, we report the updated triangles (deleted triangles and new triangles) resulting from the batch of updates. We notice that it is cost expensive to directly apply state-of-the-art triangle listing algorithms because they are designed to enumerate the complete set of triangles from a given graph, whereas only the updated ones are the relevant output for our problem setting. In this paper, we developed an efficient algorithm, namely DPTL, based on a newly designed orientation technique, which only outputs the updated triangles while ensuring that each triangle solution is identified without any duplicate solutions. We follow up by taking advantage of a graph’s degree distributions and designed a more sophisticated algorithm, namely DPTL+. We show that DPTL+ can achieve the best performance in terms of both practical performance and theoretical time complexity. Our comprehensive experiments over 28 real-life large graphs show the superior performance of the DPTL+ algorithm when compared against DPTL and two baseline solutions. Theoretically, we also show that DPTL+ has a time complexity of Θ(∑〈u,v〉∈Bmin{deg(u), deg(v)}+m) where deg(x) is the degree of a vertex x, and m is the number of edges adjacent to the vertices in the batch-update. This time complexity is more promising than that of other solutions.
Modelling Trust Frameworks for Network-IDS

Though intrusion detection systems provide actionable alerts based on signature-based or anomaly-based traffic patterns, the majority of systems still rely on human analysts to identify and contain the root cause of security incidents. This process is naturally susceptible to human error and is time-consuming, which may allow for further enumeration and pivoting within a compromised environment. Through this paper, we have augmented traditional signature-based network intrusion detection systems with a trust framework whose reduction and redemption values are a function of the severity of the incident, the degree of connectivity of nodes and the time elapsed. A lightweight implementation on the nodes coupled with a multithreaded approach on the central trust server has shown the capability to scale to larger networks with high traffic volumes and a varying proportion of suspicious traffic patterns.
Taking Heuristic Based Graph Edge Partitioning One Step Ahead via OffStream Partitioning Approach

In the modern era of big data, large-scale graph computing has become challenging because of the dramatic rise in graph data size. Graph edge partitioning (GEP) is a crucial preprocessing step to distributed graph platforms, yet it is challenging to partition the large-scale graphs. GEP has shown better partition quality than the graph vertex partitioning for the graph’s skewed degree distribution. Existing GEP approaches are classified into two as stream and offline. The former category assigns edges to the partitions based on the previously received edge information. It has less partitioning quality and is affected by stream order compared to the latter while supporting big graph partitioning. The latter uses complete knowledge of a graph during partitioning and hence has a better partitioning quality than the former; however, it does not support large-scale graphs. In this study, we propose a novel OffStream partitioning approach (OSPA) and hybrid graph edge partitioner OffStreamNH. OSPA leverages both the offline and stream graph partitioning approaches through stateful partitioning by introducing a state layer. This stateful partition state is recorded while offline is partitioning its input graph. It contains partial knowledge of previously partitioned data and is used by the stream partitioner. The OffStreamNH uses Neighborhood Expansion (NE) and Higher Degree Replicated First (HDRF) algorithms for the offline and online; respectively, with minor modifications of both algorithms. Experimental results show that OffStreamNH outperforms the state of the art stream partitioners in terms of replication factor, load balance and tolerates the effect of stream orders.
The Practice of Few-Shot Learning on Electromagnetic Susceptibility Evaluation

Few-shot learning (FSL) inspired by deep neural network and its practice on electromagnetic susceptibility evaluation are reported. By the assistance of model, metric or optimization, FSL can not only make use of the prior information, but also gain the ability of learning the pattern for distinguishing and classifying input vectors from only a few samples. For transient electromagnetic disturbances, the recurrent neural network (RNN) is involved to help with the memory of the input signal with the style of 1-D time sequence. In order to learn the proper feature of transient disturbances, a Siamese neural network containing two RNNs is built and trained, and the distance of two outputs can be measured by a preselected distance function. This makes the proposed model capable to learn the difference between normal cases and failure cases. In order to demonstrate this method, a pulse current injection testing on a certain electronic system is conducted and evaluated with the metric-based FSL. The result confirms the practicability of the proposed model, for the correct classification rate can be 83.3% with only 36 samples.
Performance Analysis of LDPC Decoding Algorithms for CCSDS Telecommand Space Data Link Protocol

This paper presents the simulation of the encoding scheme defined by CCSDS for satellite Telecommand operations and analysis of various soft decision and hard decision decoding algorithms for the same. The G(Generator), H (Parity-check) matrices are designed and verified for encoding and decoding of the message respectively. The decoding algorithms are implemented and the performances are analyzed and non-convergence of decoding algorithms, scheduling restrictions of the defined H, G matrix are explored. It is found that soft decision algorithms give 6dB coding gain and hard decision algorithms give 4 dB coding gain for the 4-bit quantized inputs.
Wide Bandgap Semiconductor-Based Converters for EV and HEV Magnetically Coupled Resonant Wireless Power Transfer

This paper evaluates the feasibility of wide-bandgap semiconductors as a replacement for silicon semiconductors, applied in a full bridge resonant converter for EV and HEV magnetically coupled resonant wireless power transfer, as a way to increase the efficiency of the process. The work consists of a series of simulations to study the impact of different input voltages, switching frequencies, and output load values on the overall efficiency of the system, for three distinct semiconductor technologies: Si MOSFET, GaN transistor, and SiC MOSFET.
Joint Impact of Input Power, PAPR, and Load Resistance on the Receiver Efficiency of Multisine Waveforms in RF Energy Harvesting

The receiver efficiency in radio-frequency (RF) energy harvesting (EH) is affected by numerous parameters such as the average RF input power and peak-to-average power ratio (PAPR) of the waveform, the EH circuit components, and the load. In this paper, we experimentally study the dependence of RF-to-DC efficiency on these parameters. We present a test-bed, comprising a signal generator and an off-the-shelf energy harvesting receiver with a diode-based rectifier, for evaluating the RF-to-DC efficiency for varying input RF power, load resistance and a number of co-phased baseband multisine waveforms (viz. PAPR). The experimental results suggest that the RF-to-DC efficiency of multisine waveforms in RF EH is significantly affected by the average input RF power and the load resistance. The high-PAPR multisine waveforms are preferable for RF EH only for a certain range of average input RF power and load resistance. In particular, for the considered rectifier, a single-sinusoid waveform yields the highest RF-to-DC efficiency for low-resistance and high average input RF power region, while high-PAPR baseband multisines are optimal for high-resistance and low average input RF power region.
NEMS Sensors Based on Suspended Graphene

Graphene has exciting potential in nanoelectromechanical system (NEMS) applications due to its unique mechanical and electrical properties as well as its ultimate thinness. In this paper, we discuss the potential of using suspended graphene structures in NEMS sensors and provide an overview of our previous research results on piezoresistive graphene NEMS sensors, including pressure sensors and accelerometers.