Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Circular Patch Microstrip Antenna (CPMA) for Partial Discharge Detector in oil insulation

Partial discharge (PD) is a localized dielectric breakdown (which does not completely bridge the space between the two conductors) of a small portion of a solid or fluid electrical insulation system under high voltage stress. While a corona discharge is usually revealed by a relatively steady glow or brush discharge in air, partial discharges within solid insulation system are not visible. PD can occur in a gaseous, liquid or solid insulating medium. It often starts within gas voids, such as voids in solid epoxy insulation or bubbles in transformer oil. Protracted partial discharge can erode solid insulation and eventually lead to breakdown of insulation. One of the PD measurements in oil insulation is using UHF method, by measuring the waves generated by PD using antenna. One of antenna having good characteristics is UWB double layer printed antenna. In this paper the application of circular patch microstrip antenna for partial discharge detection is reported. The application of antenna on PD measurement in oil insulation, shows that the antenna is able to detect PD. The characteristics of PD: PDIV, PD waveform, PD phase pattern are measured using this antenna. The VNA testing of the antenna shows that the antenna bandwidth is 3.39 GHz.
Application of Harris Hawks Algorithm for Frequency Response Enhancement of Two-Area Interconnected Power System with DFIG Based Wind Turbine

This paper presents an application of Harris Hawks Algorithm (HHA) as an intelligent method for optimal tuning of integral classical controllers to design the appropriate load frequency controllers. A mathematically model is presented for investigating the optimized controller parameters and enhancing the system performance. It consists of two-area interconnected power system with different participation of DFIG based wind turbine at the first area only. This article solves the load frequency control (LFC) problem under step load perturbation (SLP) by 2%. Simulation results confirm that, oscillation of frequency at both areas and tie-line power oscillation are effectively damped using HHA. Also, when DFIG participation level is high, the optimum performance of power system is achieved. All time domain simulation results are carried out using MATLAB program for investigating the effectiveness of this algorithm.
QLFR: A Q-Learning-Based Localization-Free Routing Protocol for Underwater Sensor Networks

Designing a routing protocol for underwater sensor networks is a great challenge due to characteristics of high energy consumption and high latency. This paper investigates a Q-learning-based localization-free routing protocol (QLFR) to prolong the lifetime as well as reduce the end-to-end delay for underwater sensor networks. Aiming to seek optimal routing policies, Q- value is calculated by jointly considering residual energy and depth information of sensor nodes throughout the routing process. More specifically, we define two cost functions (depth-related cost and energy-related cost) for Q-learning, in order to reduce delay and extend the network lifetime. In addition, a holding time mechanism for packet forwarding is designed according to the priority of forwarding nodes. The key contribution lies in: 1) a novel Q-learning-based routing protocol for UWSNs; 2) a new holding time mechanism for packet forwarding; and 3) a packet- delivery-ratio-based scheme to further reduce unnecessary transmissions. Extensive simulation results demonstrate superiority performance of our routing protocol in terms of reducing end-to-end delay and extending the network lifetime.
A Joint Balancing Flow Table and Reducing Delay Scheme for Mice-Flows in Data Center Networks

In data center networks based on SDN, mice-flows are latency-sensitive and packet loss sensitive. Meanwhile, they account for the majority of traffic in the network, most flow rules are installed to direct the forwarding of mice-flows. According to the characteristics mentioned above, this paper proposes a joint balancing flow table and reducing delay (BFTRTD) scheme for mice-flows in data center networks to efficiently utilize limited flow tables and minimize the delay for mice-flows. In this scheme, a novel evaluation index for table balance is proposed to balance flow tables, combining with the delay of the path to initialize routes. In addition, this paper also adopts the uptodate flow rules installation mechanism to further guarantee the transmission quality and delay of mice-flows. We evaluated the proposed BFTRTD in terms of average packet loss rate and average delay of mice-flows. Simulation results show that, compared with ECMP and DIFF- Mice, the proposed BFTRTD scheme reduces the average packet loss rate by an average of 4.5% and 5.9%, while decreases the average delay by an average of 4.1% and 4.7%, when the flow arrival rate is between 120 Mbit/min and 280 Mbit/min where network load goes from low to high
Linear-to-Circular Polarization Conversion For Beam-Scanning Antenna Based On Self-Complementary Metasurface

One wideband circular polarization (CP) beam-scanning antenna based on self-complementary zigzag metasurface is proposed in this paper. Technology of linear-to-circular polarization (LCP) conversion is introduced for this design. The antenna consists of two layers. The bottom layer is a linearly polarized (LP) leaky-wave antenna (LWA) with wide beam scanning range from backward to forward. The LP beam is incident on the upper self-complementary zigzag metasurface layer, and the transmitted wave is transformed into CP wave. In the operating band of 9.4-13.2 GHz, simulated scanning angle of the proposed antenna is from −17° to +24°. The 3 dB axial ratio (AR) bandwidth of the antenna is 2.1 GHz (11.1-13.2 GHz). It has potential applications in wireless communication systems for simple structure, low-cost fabrication, CP Beam scanning properties.
Prediction-based Routing Protocol for V2V Communications in Urban Environment

In VANET, the routing is a difficult problem due to unpredictable nodes as vehicles movement and frequent network topology change. Therefore, the most important issue is to predict correctly the future movements of vehicles when selecting intermediate nodes. This paper proposes a Geography Movement Prediction Routing protocol (GMPR) for V2V communication in urban environment that combines vehicle’s geography information and movement prediction. Theory analysis and experiments show that the proposed algorithm outperformed the conventional algorithms on the comparisons of packets delivery rate, average end-to-end delay and average number of hops.
Clock Synchronization Using Linear Programming, Multicasts, and Temperature Compensation

Clock (or time) synchronization is essential for many applications in the Industrial Internet of Things (IIoT). Hence, it is a vital research field and important field of standardization ambitions. The most accurate protocols like PTP and gPTP need specialized hardware to reach their maximum precision. Without this hardware, they cannot compensate massive packet delays. It was shown that approaches based on linear programming (LP) can mitigate this problem. However, changes in the clock frequency lead to nonlinear clocks, which are not well compensated by LP-based approaches. As a consequence, we propose the SLMT approach that uses LP, multicasts, and temperature compensation for time synchronization. To the best of our knowledge, SLMT is the first synchronization approach that combines LP and one-way exchange or multicasts, respectively. Consequently, it is efficient regarding the number of messages. Furthermore, to the best of our knowledge, SLMT is the first synchronization approach that combines LP with a temperature compensation in order to mitigate LP's conceptual drawback with nonlinear clocks. In an extensive evaluation and comparison to many state-of-the-art approaches, we show that SLMT outperforms these approaches, especially under harsh conditions like rapid temperature changes and unknown non-negligible network delays.
A 60-m Range 6.16-mW Laser-Power Linear-Mode LiDAR System With Multiplex ADC/TDC in 65-nm CMOS

This paper presents a linear-mode light detection and ranging (LiDAR) analog front-end architecture with multiplex analog-to-digital converter/time-to-digital converter (ADC/TDC). An added voltage-to-time converter (VTC) and a reused TDC are simultaneously used to implement the ADC and TDC function, thus replacing discrete ADC and TDC, saving hardware cost and reducing power consumption. A three-stage inverter-based transimpedance amplifier (TIA) with ultra-low-power, low-noise and high/low gain mode (long/short range mode) is proposed to reduce its influence to optical signal-to-noise ratios (OSNR). The prototype TIA and ADC/TDC is fabricated in the 65-nm CMOS technology and integrated into the single-line APD-based LiDAR system. The receiver front-end of TIA and ADC/TDC only consumes 12.44-mW. The minimum detection current of the receiver front-end is less than 238 nA with bandwidth of 150MHz for long-range and weak-light detection. LiDAR achieves a measurement range of 60 m with a 70-klx direct sunlight and only 6.16 mW average laser power. Experimental results show that this architecture is suitable for low-cost multi-line integrated LiDAR applications compared to conventional architecture using ADC, TDC, ADC+TDC architecture.
State-of-charge Estimation of Batteries Based on Open-circuit Voltage and Time Series Neural Network

Time series neural network (TSNNT) performs effectively in battery state of charge (SOC) estimation, but there are also some problems. Different data characteristics will affect the choice of time delay value of time series neural network, and the time delay value will affect the prediction accuracy and generality. The traditional RLS-based on-line open-circuit voltage (OCV) identification algorithms have some shortcomings, which will lead to larger errors in the obtained open-circuit voltage, resulting in larger errors in SOC estimation. To solve these two problems, a novel SOC estimation method based on open-circuit voltage and time series neural network is proposed in this paper. The experimental results show that the proposed method can effectively suppress the OCV error caused by RLS-based method and can also make the time series network use a larger time delay value, so as to improve the accuracy of network prediction, while ensuring the adaptability of the network.
Modeling of Utility Function for Real-Time Prediction of Spatial Information

Real-time prediction of spatial information has attracted a lot of attention. Machine learning enables us to provide real-time prediction of spatial information such as road traffic by using aggregated sensor data. The amount of mobile traffic is forecasted to increase exponentially, thereby causing serious transmission delays when traffic loads are heavy. If a part of the data used for predicting spatial information in real time does not arrive on time, the prediction accuracy degrades because the prediction is done without the missing data. A utility-based scheduling technique has been suggested as a way of prioritizing such delay-sensitive data. However, no study has not addressed the utility-based scheduling for the real-time prediction of spatial information. Therefore, this paper proposes a scheme that enables modeling the utility function for real- time prediction of spatial information. The scheme is roughly composed of two steps: the first creates training data from original time-series data and a machine learning model using the data, while the second models the utility function using the feature selection method in the learning model. Feature selection method enables extracting the importance of data in terms of how much the data contributes to the prediction accuracy. This paper assumes the road traffic prediction as a scenario and shows the utility function modeled by the proposed scheme using real spatial datasets. A numerical study demonstrates how the model of the utility function works effectively in prioritizing data for real-time prediction in terms of accuracy.
Robust (Deep) Learning Framework Against Dirty Labels and Beyond

Data is generated with unprecedented speed, due to the flourishing of social media and open platforms. However, due to the lack of scrutinizing, both clean and dirty data are widely spreaded. For instance, there is a significant portion of images tagged with corrupted dirty class labels. Such dirty data sets are not only detrimental to the learning outcomes, e.g., misclassified images into the wrong classes, but also costly. It is pointed out that bad data can cost the U.S. up to a daunting 3 trillion dollars per year. In this paper, we address the following question: how prevailing (deep) machine learning models can be robustly trained given a non-negligible presence of corrupted labeled data. Dirty labels significantly increase the complexity of existing learning problems, as the ground truth of label’s quality are not easily assessed. Here, we advocate to rigorously incorporate human experts into one learning framework where both artificial and human intelligence collaborate. To such an end, we combine three strategies to enhance the robustness for deep and regular machine learning algorithms, namely, (i) data filtering through additional quality model, (ii) data selection via actively learning from expert, and (iii) imitating expert’s correction process. We demonstrate three strategies sequentially with examples and apply them on widely used benchmarks, such as CIFAR10 and CIFAR100. Our initial results show the effectiveness of the proposed strategies in combating dirty labels, e.g., the resulting classification can be up to 50% higher than the state-of-the-art AI-only solutions. Finally, we extend the discussion of robust learning from the trusted data to the trusted execution environment.
UAV Placement and Bandwidth Allocation for UAV Based Wireless Networks

In this paper, we study the problem of unmanned aerial vehicles (UAVs) placement and bandwidth allocation for wireless networks with wireless backhaul links. The general model with different possible configurations of line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions of wireless links between UAVs and ground users (GUs) is explicitly considered based on which we derive the average rates for wireless access links. The underlying problem is a difficult non-convex optimization problem due to the strong co-channel interference and the complicated relationship between the LoS/NLoS probabilities and UAVs' locations. To solve this challenging problem, we employ the alternative optimization approach where we iteratively optimize the bandwidth allocation and UAV placement until convergence. Moreover, we employ the difference of convex functions (DC) optimization and quadratic transformation approaches to convexify and tackle the non-convex UAV placement sub-problem. Via numerical studies, we show that the proposed scheme achieves a significant throughput gain compared to a baseline in which UAVs are deployed at the centers of hotspot areas.
Intelligent Deep Learning Method for Forecasting the Health Evolution Trend of Aero-Engine With Dispersion Entropy-Based Multi-Scale Series Aggregation and LSTM Neural Network

Accurate health evolution trend forecasting of aero-engine is essential for operation reliability and maintenance costs of aeronautical equipment. In this study, an intelligent deep learning method, systematically blending the dispersion entropy-based multi-scale series aggregation scheme and long short term memory (LSTM) neural network, is proposed for forecasting the health evolution trend of aero-engine. Firstly, a comprehensive measurement of health levels, namely, integrated health state index (IHSI), is developed with high-dimensional dataset. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is exploited to decompose the IHSI sequence into several multi-scale series to further capture the internal characteristics of original sequence. Subsequently, multi-scale series aggregation assisted with dispersion entropy analysis theory is conducted for obtaining the aggregated sub-series (ASS). Finally, the ASS are served as the inputs of LSTM network to complete the health evolution trend forecasting of aero-engine. To demonstrate the effectiveness of the proposed method, six approaches are present for the comparisons of forecasting performance. The experimental results indicate that the proposed method can effectively measure the health evolution process of aero-engine and further obtain more accurate trend forecasting results.
Optimization of Multi Objective Load Frequency Control Using Cuckoo Search Algorithm

The present work emphasis on multi-objective frequency controller to regulate operating frequency by controlling the governor settings. Goal of PID controller is to retain the frequency deviation within bounds in a short span of time period, since interconnected power system has many frequency dependant loads. However, the proposed controller design also aimed at minimum peak overshoot, shortest settling time and stability of system. A multi objective function was considered by confining these dynamic response specifications. Optimization of multi-objective function was carried with the aid of soft computing methodologies. Superiority of multi-objective function was tested by generating different test cases using Cuckoo Search optimization (CS) algorithm. The proposed multi-objective function performs better and reasonably maintains good equilibrium between frequency changes and transient oscillations.
A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capital and operational expenditure. Proactive network optimisation is widely acknowledged as one of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area.
Two Stream Convolutional Neural Network for Full Field Optical Coherence Tomography Fingerprint Recognition

Full-Field Optical Coherence Tomography (FF-OCT) for fingerprint imaging has been recently explored to counter presentation attacks (previously referred as spoofing attacks). The ability to acquire discriminant information under the surface of the external fingerprint can not only detect such attacks, but also provide supplementary information to make fingerprint recognition superior. In this work, we present a new approach for robust fingerprint recognition by learning deep representation by employing multiple subsurface fingerprints. Specifically, we design a new Two Stream - Convolutional Neural Network (TS-CNN) to employ internal fingerprint images captured at 6 different depths. Further, to accelerate the learning of the features, we transfer the weights of the AlexNet for each stream. With a semi-public in-house FF-OCT database of 200 unique fingerprints, we demonstrate the applicability of the proposed approach by achieving 0.17% Equal Error Rate (EER). While the proposed TS-CNN is trained and fine-tuned on a development subset of fingerprints from FF-OCT fingerprint image database, the final results are reported on the disjoint testing set of fingerprints from the same dataset.
Trajectory Design for Overlay UAV-to-Device Communications by Deep Reinforcement Learning

In this paper, we consider a cellular Internet of unmanned aerial vehicles (UAVs) where the sensory data can be transmitted to the mobile devices directly by overlaying UAV-to-Device (U2D) communications, or through the base station (BS) by cellular communications. Since the transmission modes of UAVs may influence their trajectories, we study the trajectory design problem for UAVs aiming to maximize the total utility in consideration of their transmission modes. This problem is a Markov decision problem (MDP) with a large state-action space, and thus, we propose a multi-UAV trajectory design algorithm using multi- agent deep reinforcement learning (DRL) to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than the single-agent method.
An Auction Scheme for Computing Resource Allocation in D2D-Assisted Mobile Edge Computing

To satisfy the explosively mounting requirements of computing resources, the device-to-device (D2D) communications are integrated into the mobile edge computing (MEC) system to share the computing resources of mobile devices. However, most existing literature neglects the selfish of mobile devices. They are reluctant or even refuse to help others. Motivated by this, we propose an auction scheme for computing resource allocation (ASCRA) in D2D- assisted MEC system. The ASCRA algorithm consists of three steps which are identification confirmation, candidate selection, and matching & pricing. There are two kinds of sellers in ASCRA algorithm, where both mobile devices and edge server can share and sell their computing resources. The edge server is also the auctioneer of the system, which controls and manages the whole system. Moreover, both the resource condition and the delay condition are considered in ASCRA algorithm. Numerical results show that the system efficiency, individual rationality, budget-balanced and truthful properties are satisfied in our ASCRA algorithm.
On the design of silicon Mach-Zehnder interferometers for the C-band

A silicon Mach-Zehnder interferometer is designed, fabricated and experimentally demonstrated for the C-band. Several interferometers were designed with different free spectral ranges. The influence of the waveguide width was carefully analyzed with respect to the performance of the interferometer and a study about the deviation of the design parameters of the waveguide was made.