Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Separating target speech from noisy signal is important for many realistic applications. Recently, deep neural network (DNN) has been widely used in speech enhancement (SE) and obtained prominent performance improvements. However, the current deep models require a large amount of training data to obtain a good performance. It is still challenging to construct an effective deep speech enhancement model with actual few training samples. At present, meta-learning has become the research focus of few-shot learning due to its capability of quickly process new tasks with few samples by the prior meta-knowledge, but there are very few works applying meta-learning on few-shot speech enhancement. In this paper, we propose a generic meta-learning framework Meta-SE which applies the U-Net as the meta-learner, to tackle the few-shot speech enhancement problem. Meta-SE is trained and optimized with the changed speech enhancement tasks to obtain meta-knowledge, and towards better capability of fast and good generalizing to the new unseen noises with few training samples. The experiment results show that the proposed method not only outperforms the state-of-the-arts DNN-SE models under the few-shot conditions, but also learns a more general and flexible model for task adaption.
Planning Ward and Intensive Care Unit Beds for COVID-19 Patients Using a Discrete Event Simulation Model

This paper reports the construction of a simulation model used to support the decision-making concerned with the short-term planning of the necessary hospital beds to face the COVID-19 in Navarre, Spain. The simulation model focusses on estimating the health system’s transitory state. It reproduces the outbreak dynamics by using the Gompertz growth model and the patient flow through the hospital, including the possible admission in the Intensive Care Unit (ICU). The output estimates the number of the necessary ward and ICU beds to provide healthcare to all patients for the next days. The simulation model uses expert opinions at the first stages of the outbreak, but as more data are collected the necessary parameters are fitted by statistical analysis or combining both. Every day, the research team informed the regional logistic team in charge of planning the health resources. Based on these predictions the authorities plan the necessary resources.
A Novel UAV-Enabled Data Collection Scheme for Intelligent Transportation System Through UAV Speed Control

The rapid and convenient travel of people and the timely transportation of goods depend on the correct decision of the Intelligent Transportation Systems (ITS). Due to the decision-making of ITS requires a large amount of data to support, UAV-enabled periodic data collection is an effective method. However, due to the limited resources of UAV, UAV cannot directly collect data from all storage devices, resulting in unfair data collection. Therefore, we propose a UAV Speed Control based Fairness Data Collection (USCFDC) scheme. First, since the fairness of data collection will affect the decision-making of ITS, a framework for controlling the flight speed of the UAV is proposed to improve the fairness of data collection. The flight speed of UAV will slow down in areas with a large number of nodes, thereby improving the fairness of data collection. Second, a novel method is proposed to maximize the amount of data collected by UAV from each node. With this method, the value of the amount of data will be used as the dichotomous value in the dichotomy algorithm, and the UAV must collect a certain amount of data from each node. The upper and lower limits of the dichotomy algorithm are adjusted according to the time duration for UAV to collect data. Compared with previous schemes, the fairness of data collection can be improved by a maximum of 15.89% under the same flight time of UAV. Besides, the energy consumption is reduced by 49.31%–52.55% and the flight time of the UAV is reduced by 48%–62.38% when the amount of collected data is the same.
A 6-bit CMOS Phase Shifter with Active Balun and Three-Stage Poly-Phase Filter for Phased Arrays

A 6-bit high resolution active phase shifter using 0.18 μm CMOS process is presented for P-band phased array applications. An improved active balun converts single-ended signal to differential with high balance, wideband input/output matching and compact area. Three-stage poly-phase filter (PPF) outputs high quality broadband orthogonal signals among 300-800 MHz frequency range. Consequently, a phase shift with high phase shifting accuracy and low gain/phase errors can be obtained. Measurements show that the RMS phase error of the active phase shifter is < 3.5° over 400-600 MHz and ＜5° in 300-800 MHz. The measured RMS magnitude error is below 1 dB and the mean gain is 4~5.5 dB in 300-800 MHz. The measured input and output return loss are both ＜-12 dB at 300-800 MHz. The total power consumption is 14.7 mW under a supply of 1.8V. The chip size is 1.3×1 mm2 including pads.
SEP of the M-ary θ-QAM Signals Under Nakagami-q Fading and AWGN Noise

In this article, an upper bound is obtained, by means of the union bound (UB), for the symbol error probability (SEP) of the M-ary $\theta-$quadrature amplitude modulation ($\theta-$QAM) scheme, in channels subject to additive white Gaussian noise (AWGN) and Nakagami-q fading. In addition, an exact and closed-form expression for the pairwise error probability (PEP) of the system is derived. Curves of SEP are presented, under different parameters that characterize the channel.
Drowsiness Detection System using Visual Articulators

Many people lose their lives due to fatal road accidents. One of the main causes of these accidents are the drowsiness of the driver that are caused due to lack of sleep or the fatigue which reflects immediately on the driving effect. To avoid the drowsiness of the driver, an efficient system detects and alerts the user. Further, driver has to be prevented from falling asleep again while driving. Hence, a novel system is proposed to detect the drowsiness and provide alarms to alert the drivers. An inbuilt ChatBot which is proposed that gives a simple conversation with the driver. A camera enables the facial landmarks and the eye-lip region extraction is performed where the eye-lip aspect ratio is computed. This initiates the alarms in the form of Wecker sound and three such subsequent detections issues an SMS to the driver’s kin or a provided mobile number. This paper analyzes the performance of the proposed system and compares the results with low and high glare conditions.
A Pipeline Leak Detection and Localization Approach Based on Ensemble TL1DCNN

There is an increasing need for timely pipeline leak detection and localization methods, pipeline leak could lead to not only the loss of the goods but also considerable environmental and economic problems. With the rapid development of hardware and software, the pipeline leak detection and localization algorithms have been widely researched and applied in many Fields. However, traditional methods are usually limited by extracting features manually, which is inefficient and time-consuming. Convolutional neuron network is an effective method to extract features automatically. In this paper, a novel ensemble transfer learning one-dimension convolutional neural network (TL1DCNN) for the pipeline leak detection and localization is proposed. The TL1DCNN plays the role of base learner. The results of a set of obtained base learners are integrated to achieve the task of pipeline leak detection and localization. Firstly, one-dimension convolutional neural network (1DCNN) models with different parameters are pretrained with source domain data. A small learning rate is set to retrain the above 1DCNN models for target task with target domain data in order to obtain TL1DCNN base learners. Then, the four ensemble strategies with different number base learners whose ensemble weights are optimized by particle swarm optimization algorithm are obtained by minimizing the sum of similarity. The dataset simulated by pipeline network model is used to evaluate the effectiveness of the proposed approach. The indicators such as classification accuracy, precision, recall, F_score and confusion matrix are used to compare the proposed approach with traditional methods and other deep learning methods. The experimental results show that the performance of the proposed approach is superior to other compared methods.
Design of broadband Frequency Conversion Components based on SIP

This paper proposes a miniaturization design method of SIP-based broadband inverter components, built on AL2O3 ceramic substrate, using three-layer stacking and RF multi-layer transition design, to realize the more than 1GHz IF broadband frequency conversion transmission of Ku. Through the electromagnetic simulation of packing key components and thermal simulation of multilayer circuit boards with different material, it fully shows that the substrate interconnection technology using HTCC process can meet the indicator requirements of the frequency conversation components. This method provides a good support for the miniaturization system package of the next high-power device and lays the foundation for the system of the next generation RF front end.
A High-Precision Magnetic Induction Through-the-Earth Positioning Scheme

Through-the-earth positioning technology plays a vital role in mine rescue. To achieve through-the-earth positioning in the complex earth that contains geomagnetic noise, water, and rocks, we design a magnetic induction through-the-earth positioning scheme that includes positioning methods and a noise reduction method. The positioning scheme achieves three-dimensional through-the-earth positioning with high precision and long distance. Using the directional characteristics of the magnetic field vector distributed by the transmitter coil horizontally placed underground, we design two positioning methods to determine the horizontal two-dimensional position and vertical dimensional position of the transmitter, respectively. Taking advantage of the extremely narrow bandwidth of the sinusoidal signal in the spectrum, we design a noise reduction method to reduce the influence of noise on positioning accuracy. The simulation results show that the positioning error of the positioning method based on the signal direction is less than that of the positioning methods based on the equations, path loss, and the optimization algorithm, and the noise reduction performance of the frequency-point amplitude acquisition method is better than that of the bandpass filter. The simulation results also show that the positioning scheme proposed in this paper can penetrate 1000 meters of the earth to achieve high-precision through-the-earth positioning with errors of less than 2 meters.
Design of a Novel TE31 to TM01 Mode Converter with High Power Capacity

A novel mode converter from TE31 mode to TM01 mode in circular waveguide is designed for high power microwave (HPM) system application in this paper. This mode converter is designed based on the mode matching analysis, where the tangential electric field and magnetic field components are continuous on the discontinuous interface of a circular-to-rectangular waveguide junction. The cylindrical TE31 mode is firstly transformed into six ways of rectangular TE10 mode, and then the six ways of TE10 mode are synthesized and converted into the output cylindrical TM01 mode. The maximum conversion efficiency can overreach 99% in the working bandwidth from 1.53GHz to 1.61GHz. The power capacity of this mode converter can reach 1.6GW when it inflates SF6 gas with one atmospheric pressure.
A Broadband High-Gain H-plane SIW Horn Antenna

In this paper, a novel H-plane substrate integrated waveguide (SIW) horn antenna loaded with tapered periodic leaky-wave strips (TPLS) and a dielectric slab is proposed. The mismatching between the horn aperture and free space caused by the thinness of the substrate, leads to narrow impedance bandwidth and poor radiation performance for SIW horns. To enhance the bandwidth and gain performances, the TPLS are printed in front of the horn aperture, which guide and leak the electromagnetic waves gradually along the dielectric slab. Moreover, an extended dielectric slab placed in front of the TPLS further enhances the realized gain by more than 5 dBi. Simulated results illustrate that an impedance bandwidth of 60.8% is obtained from 20.6 GHz to 38.6 GHz with the return loss |S11| below -10 dB. Meanwhile, a peak realized gain of 19.1 dBi is achieved at the frequency of 29 GHz, with the realized gain more than 9 dBi across the whole operating band. In addition, stable radiation patterns in both E-plane and H-plane are observed over the entire operating band.
The Design of Dual Band Frequency Selective Surface

This paper proposes a frequency selective surface (FSS) design with two transmission bands which operate at 4.86~7.43GHz and 10.69~12.69GHz, respectively. To exhibit the performance of the proposed FSS model we also make an analysis by equivalent circuit method (ECM). We illustrate the relationship between the FSS structure and the equivalent circuit. There is a good matching from the frequency response of equivalent circuit and the S12 parameters of FSS structure. Finally we use the surface current distribution to make a more detailed description of the characteristics of the frequency selective surface.
Efficient Time Series Clustering by Minimizing Dynamic Time Warping Utilization

Dynamic Time Warping (DTW) is a widely used distance measurement in time series clustering. DTW distance is invariant to time series phase perturbations but has a quadratic complexity. An effective acceleration method must reduce the DTW utilization ratio during time series clustering; for example, TADPole uses both upper and lower bounds to prune off a large ratio of expensive DTW calculations. To further reduce the DTW utilization ratio, we find that the linear-complexity L1-norm distance (Manhattan distance) is effective enough when the time series only comprise small phase perturbations. Therefore, we propose a novel time series clustering by Minimizing Dynamic Time Warping Utilization (MiniDTW) algorithm to accelerate time series clustering. In MiniDTW, the dataset is first $greedily$ summarized into seed clusters, which comprise time series of small phase perturbations, by L1-norm distance. Then, we develop a new Sparse Symmetric Non-negative Matrix Factorization (SSNMF) algorithm, which factorizes the DTW distance matrix of seed cluster centers, to merge the seed clusters into the final clusters. The experiments on UCR time series datasets demonstrate that MiniDTW, pruning 98.52% of the DTW utilization, is better than the counterpart method, TADPole, which only prunes 75.56% of the DTW utilization; and thus MiniDTW is 10 times faster than TADPole.
Combating Misinformation Dissemination through Verification and Content Driven Recommendation

The COVID 19 pandemic is a humanitarian emergency that poses an enormous threat to society and has impacted various social media platforms and journalism. News and social media has become an immensely popular platform for consumption of information. However, these platforms are also the bearer of fake news and information which causes negative effects and creates panic. Thus, this research work aim to tackle this problem by creating a unique hybrid model using Machine learning algorithms with Natural Language Processing (NLP) techniques to verify news. In order to make the proposed system foolproof, a superior content based recommendation system is developed which will encourage users to consume authenticated news and content from verified sources. Thus, such a system will provide a holistic approach as it not only verifies but also provides genuine and true recommendations for the same.
The Economic and Environmental Impact of Bitcoin

The controversies surrounding Bitcoin, one of the most frequently used and advertised cryptocurrency, are focused on identifying its qualities, the advantages and disadvantages of using it and, last but not least, its ability to survive over time and become a viable alternative to the traditional currency, taking into account the effects on the environment of the technology used to extract and trade it. Based on such considerations, this article aims to provide an overview of this cryptocurrency, from the perspective of conducting a systematic review of the literature dedicated to the economic and environmental impact of Bitcoin. Using peer-reviewed articles collected from academic databases, we aimed at synthesizing and critically evaluating the points of view in the scientific literature regarding the doctrinal source of the emergence of Bitcoin, the identity of this cryptocurrency from an economic point of view, following its implications on the economic and social environment. Subsequently, this research offers the opportunity of evaluating the level of knowledge considering the impact of Bitcoin mining process on the environment from the perspective of the energy consumption and CO2 emissions, in order to finally analyze Bitcoin regulation and identify possible solutions to reduce the negative impact on the environment and beyond. The findings suggest that, despite high energy consumption and adverse environmental impact, Bitcoin continues to be an instrument used in the economic environment for a variety of purposes. Moreover, the trend of regulating it in various countries shows that the use of Bitcoin is beginning to gain some legitimacy, despite criticism against this cryptocurrency.
IoT based Smart Accident Detection & Insurance Claiming System

IoT based Smart Transport Systems are receiving a huge attention among public. Owing to ever growing vehicle rate, a momentous growth in the number of accidents is also increasing. Vehicles implanted with huge number of sensors permit the people to monitor the present state of the vehicle and its environment. These sensors even safeguard the people from accidents by dispatching the Airbags during the time of accidents. But it’s been a tedious process to deal with the incidents that happens after the occurrence of accidents. It takes time to inform about the accident to help centre and arrange ambulance for attending the people who are injured during the accident. This may lead to increase in fatality rate. In addition to this, insurance claiming process is also tedious for many people. Sometime people also escape from law after involving in life threatening accident and making the criminal identification task difficult for police officers. In order to deal with all these problems, IoT based Smart Accident Detection and Insurance Claiming System (ISADICS) is introduced in this paper. This system automatically informs the nearby hospital, police department and insurance company to arrive to accident spot quickly and perform their duties.
Managing Energy Consumption of Wireless Multipath TCP Connections Using Software-Defined Networking: A Review

Multipath transmission control protocol (MPTCP) has been established as an extension to the standard TCP to achieve efficient and uninterrupted communication simultaneously through multiple paths. It outperforms traditional TCP in terms of performance and reliability. On the other hand, the emergence of software-defined networking (SDN) has made a momentous change in traditional network management and control. It makes the networks programmable via a controller that manages the entire network. However, energy consumption represents a significant issue when using multihomed devices since most of the wireless devices used in emerging technologies such as Internet-of-things and fog computing are battery-powered. This paper aims to answer the question of whether the current literature addresses controlling the energy consumption of MPTCP connections using SDN. Thus, the paper reviews the current research works that target MPTCP energy consumption for wireless connections and the usage of SDN to manage those connections. Our review finds that energy efficiency remains an open research challenge while controlling it in multipath devices effectively by using SDN is not well explored in the literature.
Local Structural Optimization Method Based on Orthogonal Analysis for a Resistant Muffler

A resistant muffler is the most direct and effective means of reducing exhaust noise from earthmoving equipment. Different types of construction machinery require different mufflers. Current muffler design is mostly experience-based, and there are blind spots in the design process. The optimization scheme must constantly be revised to achieve satisfactory noise reduction. The use of a large number of optimization schemes are wasteful in terms of both cost and time. Therefore, an effective and time-saving design method for muffler optimization is needed. An optimization approach, known as the “local structural optimization method for a resistant muffler”, based on orthogonal analysis is proposed in this study to optimize the design of a resistant muffler with a complex structure. The results of an orthogonal analysis for a simulation of the muffler structure show that the muffler acoustic and aerodynamic performances are affected by the inlet pipe, outlet pipe, insertion pipe, cross-flow perforated pipe and other local structures, and the results are employed to determine the influence law for the transmission loss (TL) and the exhaust back pressure, considering the local structural parameters of the muffler. The method is applied to optimize the design of a muffler suitable for an excavator and verified by performing an installation test. The experimental results show that the improved designs reduces the exhaust noise by 5 dB and the exhaust back pressure by 0.6 kPa. The improved designs exhibit higher aerodynamic and acoustic performances than a muffler prototype, showing that the proposed method is accurate, effective and reliable. The proposed method considerably simplifies the design process and saves time and cost. This method provides theoretical guidance and an optimization process for the design of a muffler for construction machinery.
Kerr Optical Frequency Combs With Multi-FSR Mode Spacing in Silica Microspheres

We experimentally demonstrate Kerr optical frequency combs (OFCs) with mode spacing of 1, 2, 3, 5, 6, and 8 free spectral ranges (FSRs) corresponding to 0.4, 0.8, 1.2, 2, 2.4, and 3.2 THz, respectively, in silica microspheres pumped by CW C-band lasers in the anomalous dispersion range. Experimental realizations are based on using standard telecom equipment and components such as SMF-28e fiber for producing microspheres and fiber tapers for pump coupling and OFC outcoupling. Numerical simulation is performed to support the experimental results and explain the observed spectral asymmetry in OFCs by the influence of the 3rd-order dispersion.
Numerical Analysis of flat Dipole antenna for UWB Applications

In this paper flat dipole antenna is designed and analyzed numerically for microwave applications. The dipole is a resonant antenna with narrow bandwidth. In this paper the proposed dipole is thin and flat. The impedance bandwidth of the antenna is analyzed for different sizes of the element. The length of the antenna is fixed and its width is changed. The radiation properties are analyzed for Ultra-Wide Band (UWB) range. The proposed element exhibits wide band width with increase in the width of the strip. This element is compact and is easily mountable in portable devices.