Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Any Source Overlay Multicast In CRT-Based P2P Networks - A Capacity-Constrained Approach

In this work, we have considered designing a highly efficient capacity-constrained overlay multicast protocol; it is designed specifically for an existing Chinese Remainder Theorem (CRT) - based structured P2P architecture [9]. Such an architecture has been the choice for its structural advantages over DHT-based architectures. It is worth mentioning its most important advantage from the viewpoint of speed of communication, that is its diameter, which is only 3 overlay hops. The protocol is not restricted to a single data source and it incorporates peer heterogeneity as well. We have also designed another any source capacity-constrained multicast protocol that incorporates the unique idea of transforming the multicast problem to a broadcast one [10]. We have compared analytically the performance of the two protocols; it has led to the observation that use of this unique idea [10] cannot take advantage of all structural advantages of a CRT-based system resulting in decrease in performance. However, the proposed multicast source discovery protocol has used this unique idea offering good performance.
A Ka-Band CW 15.5W 15.6% Fractional Bandwidth GaN Power Amplifier MMIC Using Wideband BPF Inter-stage Matching Network

This paper has reported a Ka-band 15.5W power amplifier Monolithic Microwave Integrated Circuit (MMIC) using 0. 15μm Gallium Nitride (GaN) HEMT with 15.6% fractional bandwidth. To realize a wideband power amplifier in Ka-band, a wideband band-pass filter (BPF) inter-stage matching network (ISMN) consists of the equivalent circuit parameters of inter-stage transistors is proposed. As a result, the fabricated 3-stage single-ended power amplifier MMIC using GaN-HEMT achieves a measured saturation output power (Psat) of 41.9 - 42.2dBm (15.5-16.6W) and peak power added efficiency (PAE) of 16.1 - 20.3% over 15.6% bandwidth in 26.5-31GHz under Continuous Wave (CW) operation. To the best of author’s knowledge, that 15.5W output power with 15.6% bandwidth is state-of-the art for GaN power amplifier MMIC under CW operation in Ka-band.
Towards Efficient Parallel Multipathing: A Receiver-Centric Cross-Layer Solution to Aid Multipath TCP

Applying Multipath TCP (MPTCP) towards transport-layer parallel multipath transmission to increase the throughput performance of mobile devices has attracted considerable attention. Significant results in this area have resulted in many highly promising solutions; however most of the existing solutions follow the conventional sender-centric design and the strict layering principle, without considering the fact that the receiver's intelligence and cross-layer activities can result in distinct performance advantages. In this paper, we propose MPTCP-RC, a receiver-centric cross-layer solution to aid MPTCP towards efficient parallel multipath data transmission in a wireless environment. In MPTCP-RC, the receiver performs path usage decision and cross-layer activity, by making use of its acquired first-hand knowledge of both transport layer and MAC layer, rather than only giving feedback to the sender then waiting for the decision. We evaluate and demonstrate the benefits of our proposal by simulations.
Synchronization Control Stability Analysis of Power Converters with High Renewable Energy Penetration

This paper discusses synchronization stability in power converters with high penetration of renewable energy. Grid synchronization and input/output power balancing of converter are accomplished by phase locked loop (PLL) and power control loop, respectively, while these two aspects are intrinsically related due to the mechanism of PLL based synchronization and the power/angle relationships in AC system. The performance of PLL, which is revealed to be analogous to rotor motion of synchronous generator with power control acting on PLL input, is identified as essential factor that affects power and synchronization stability in weak grid characterized by large PLL operating power angle. PLL loss of stability could occur due to the imbalance of the output component with the input component, and further leads to failure in power control.
A Novel Randomize Hierarchical Extension of MV-HEVC for Improved Light Field Compression

This paper presents a novel scheme for light field compression based on a randomize hierarchical multi-view extension of high efficiency video coding (dubbed as RH-MVHEVC). Specifically, a light field data are arranged as a multiple pseudo-temporal video sequences which are efficiently compressed with MV-HEVC encoder, following an integrated random coding technique and hierarchical prediction scheme. The critical advantage of proposed RH-MVHEVC scheme is that it utilizes not just a temporal and inter-view prediction, but efficiently exploits the strong intrinsic similarities within each sub-aperture image and among neighboring sub-aperture images in both horizontal and vertical directions. Experimental results consistently outperform the state-of-the-art compression methods on benchmark ICME 2016 and ICIP 2017 grand challenge data sets. It achieves an average up to 33.803% BD-rate reduction and 1.7978 dB BD-PSNR improvement compared with an advanced JEM video encoder, and an average 20.4156% BD-rate reduction and 2.0644 dB BD-PSNR improvement compared with a latest image-based JEM-anchor coding scheme.
Extremely Low Effective Material Loss of Air Core Photonic Crystal Fiber for THz Guidance

PCF is the ideal medium for guiding THz wave because the PCF’s has surprising light guiding properties with low material loss characteristics but proper design of PCF is the main issue to achieve minimum material loss. In this article, we proposed a PCF design for THz waveguide with extremely low EML, which is 0.016 $cm^{-1}$ at ITHz and 0.013c $m^{-1}$ at 0.8 THz, which also gives an average core power fraction near about 40%, when we considered the core porosity 81%. Moreover, we observed the change of effective area for different THz frequencies. Here we also analyze the value of EML, Core Power Fraction and Effective Area by varying porosity at different frequencies.
Analysis of the Impregnation Process of Cellulosic Materials by Ester-Based Insulating Liquids

Ester insulating liquids have higher viscosities than mineral oils. This complicates the impregnation of cellulosic insulation and has slowed down the adoption of these dielectrics in high voltage power transformers. This paper studies the dependence of the impregnation characteristics of a mineral oil, a natural ester and a synthetic ester on temperature. For this purpose, impregnation experiments were carried out and the results were compared with an analytical model. The main factors governing the impregnation mechanism related to dielectric liquids are surface tension and viscosity. Results show that increasing the temperature effectively reduces the viscosities of esters, allowing the performance of the esters to approach that of mineral oil. In addition, an exponential model is proposed to predict the impregnation rates associated to a given temperature.
Single Pulse Charge Pumping Measurements on GaN MOS-HEMTs: Fast and Reliable Extraction of Interface Traps Density

In this article, we report on the single pulse charge pumping (SPCP) measurements as a method to extract the interface trap density (<inline-formula> <tex-math notation="LaTeX">${N} _{\text {it}}$ </tex-math></inline-formula>) on the GaN metal–oxide–semiconductor high-electron-mobility transistors (MOS–HEMTs). The electron capture and emission processes are monitored in the time domain and studied during the rising and falling edges of a gate voltage pulse. Two different gate stacks are studied by SPCP including epitaxial Mg<sub>0.25</sub> Ca<sub>0.75</sub>O (MgCaO) and amorphous Al<sub>2</sub>O<sub>3</sub>. The signature charge pumping (CP) current peaks are observed enabling a direct extraction of <inline-formula> <tex-math notation="LaTeX">${N} _{\text {it}}$ </tex-math></inline-formula> as low as <inline-formula> <tex-math notation="LaTeX">$1.4\times 10^{{11}}$ </tex-math></inline-formula> cm<sup>−2</sup> with gate voltage sweeping from OFF-state to ON-state in the GaN MOS-HEMT with epitaxial MgCaO gate stack. A significant reduction of <inline-formula> <tex-math notation="LaTeX">${N} _{\text {it}}$ </tex-math></inline-formula> by the MgCaO gate stack compared to Al<sub>2</sub>O<sub>3</sub> only gate stack is also confirmed by the SPCP method. SPCP realizes a direct <inline-formula> <tex-math notation="LaTeX">${N} _{\text {it}}$ </tex-math></inline-formula> measurement on GaN transistors and confirms the high quality interface between the single crystalline epitaxial MgCaO and GaN. It is verified as a fast and reliable interface characterization method on III–V HEMTs, gate-all-around nanowire transistors, and 2-D transistors, which do not exhibit body contacts as the conventional Si transistors needed for conventional CP measurements.
A Hybrid Pre-loop/Post-loop Filtering Strategy Based Type-1 PLL for Synchronization Under Distorted Grid Conditions

Phase-locked loops (PLLs) are popular for the synchronization and control of grid connected equipment and systems. The type-1 PLL is the simplest PLL, which is characterized by having only one integrator in its control loop. Filters can be added, either inside the PLL's control loop as the in-loop filters or before the PLL's control loop as pre-loop filters, to improve the disturbance rejection capability of the type-1 PLL. However, the in-loop/pre-loop filtering strategy based type-1 PLL has a tradeoff between settling time and harmonic attenuation. To improve the performance of the type-1 PLLs, a novel filtering strategy of adding filters after the PLL's control loop as the post-loop filters is suggested, and a hybrid pre-loop/post-loop filtering strategy based type-1 PLL (HF-T1-PLL) is designed. Theoretical analysis and experimental results validate that the HF-T1-PLL can track the grid frequency and phase accurately in about 1.5 cycles under severe distorted grid conditions.
Data-Retention-Voltage-Based Analysis of Systematic Variations in SRAM SEU Hardness: A Possible Solution to Synergistic Effects of TID

Single-event upset (SEU) hardness varies across dies, wafers, and lots—even just after fabrication and further across time. Mechanisms of postfabrication variations include total ionizing dose (TID) effects, which are caused by long-term radiation exposure. This synergistic effect of TID on SEU hardness is a particular concern in integrated circuits used in space and nuclear radiation environments. This article shows that an electrical parameter called the data-retention voltage is useful in dealing with such TID effects on the SEU hardness of static random access memories (SRAMs), which are known to be particularly radiation-sensitive. Experiments showed that TID-induced variations in SRAM SEU hardness, i.e., variations in SEU cross sections, were predicted by measuring the data-retention voltage. In addition, these variations were canceled out by adjusting the power supply voltage according to its interesting relationship to the data-retention voltage. Results suggest that it might be possible in flight to predict and cancel out SEU hardness variations caused by TID and other synergistic effects.
An Efficient Algorithm for Locating TE and TM Poles for a Class of Multiscale Inhomogeneous Media Problems

The Mittag–Leffler (ML) expansion method has been minimally studied in the electromagnetic theory community. Therefore, in this article, the ML expansion functions are effectively utilized to locate initial poles for practical dielectric materials backed by a ground plane. The initial poles from the ML expansion and other existing methods are, then, checked for convergence, and found that the proposed method is highly efficient for electrically thick substrates (ETSs). To understand why, a fundamental and intrinsic convergence (in terms of relative or absolute errors) analysis of ML expansion and Taylor series expansion functions has been carried out and it was found out that the ML expansion converges significantly faster. In addition, Padè approximant has been successfully applied at no computational expense to improve the accuracy of initial pole locations. Moreover, proper and improper surface wave (SW) modes have been clearly distinguished from the poles obtained using the ML expansion method. Finally, the spectral region between leaky and SW poles for ETSs is identified using the initial pole locations obtained from using the ML expansion function on the transverse electric characteristic mode equation.
Deterministic Scheduling With Optimization of Average Transmission Delays in Industrial Wireless Sensor Networks

As a key characteristic for industrial wireless sensor networks, deterministic scheduling aims to ensure that real-time data flows arrive at destination devices under deadline constraints by allocating necessary communication resources, such as time slots and channels. Current research on deterministic scheduling mainly focuses on how to obtain a feasible scheduling solution. However, optimizing average transmission delays under deterministic flow deadlines is rarely considered when multiple scheduling solutions exist. To address this issue, in this paper we propose two scheduling algorithms: branch and bound based on link conflict classification, and least conflict degree first. The prior algorithm obtains optimal schedulable ratio by constructing a search tree and adopting necessary conditions of scheduling. The latter algorithm dynamically adjusts the scheduling order of flows to distribute channels in a heuristic manner, and achieves approximate optimal schedulable ratio in a short time with low complexity. Simulation results show that both of the proposed algorithms effectively reduce the average transmission delays of real-time data flows while guaranteeing that all flows are delivered before their deadlines.
Industrial Transfer Learning: Boosting Machine Learning in Production

In the field of production, machine learning offers great potentials to develop innovative solutions for optimization or automation. However, it faces challenges with regard to the availability of data and the high training effort of the learning models in the event of changes in the production process. In this paper, we address these challenges by introducing deep transfer learning for production. We demonstrate its potentials and benefits in a real application for predictive quality in injection molding and propose a novel approach for the continual training of neural networks across manufactured products. By creating a neural network that leverages knowledge from previous products without forgetting them, the approach shows better learning rates and more accurate predictions while requiring much less data for training. Our code is publicly available1 to reproduce our results and build upon them.
Airplane Boarding Method for Passenger Groups When Using Apron Buses

This paper proposes a method for reducing the time to complete the boarding of a two-door airplane when its passengers are transported from the airport terminal to the airplane using two apron buses. In contrast to other methods that assign passengers to apron buses, our method considers groups of passengers traveling together (e.g. families). In particular, we propose a mixed integer programming (MIP) model that assigns each group of passengers (including each single-passenger group) to one of the two apron buses based on their seating assignments. We assume that all seats on the apron buses and the two-door airplane are occupied. We conduct stochastic simulation experiments with the proposed MIP-based method and with a baseline method that assigns groups of passengers with seats furthest from one of the airplane doors to the first apron bus and assigns remaining groups to the second apron bus. Numerical results indicate that the proposed MIP-based method reduces the boarding time by up to 27.31% when compared with the baseline approach.
Total-Ionizing-Dose Effects and Low-Frequency Noise in 30-nm Gate-Length Bulk and SOI FinFETs With SiO2/HfO2 Gate Dielectrics

Total-ionizing-dose (TID) effects and low-frequency noise are evaluated in 30-nm gate-length bulk and silicon-on-insulator (SOI) FinFETs for devices with fin widths of 10–40 nm. Minimal threshold voltage shifts are observed at 2 Mrad(SiO2), but large increases in low-frequency noise are found, and significant changes in defect-energy distributions are inferred. Radiation-induced leakage current is enhanced for narrow- and short-channel bulk FinFETs. Short-channel SOI FinFETs show enhanced degradation compared with longer-channel devices. Narrow- and short-channel SOI devices exhibit high radiation tolerance. Significant random telegraph noise (RTN) is observed in smaller devices due to prominent individual defects.
Delay-Optimized V2V-Based Computation Offloading in Urban Vehicular Edge Computing and Networks

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. In view of the delay-sensitive property of these promising applications, as well as the high expense by using infrastructures and roadside units (RSU), the task offloading among vehicles has gained enormous popularity considering its free-of-charge and timely response. In this paper, by utilizing the gathering period of vehicles in urban environment due to stopped by traffic lights or Area of Interest (AOI), a task offloading scheme merely relying on vehicle-to-vehicle (V2V) communication is proposed by fully exploring the idle resources of gathered vehicles for task execution. Through formulating the task execution as a Min-Max problem among one task and several cooperative vehicles, the task executing time is optimized with the Max-Min Fairness scheme, which is further solved by the Particle Swarm Optimization (PSO) Algorithm. Extensive simulation demonstrate that our model could well meet the delay requirement of delay-sensitive application by cooperative computing among vehicles.
Traffic Density Investigation & Road Accident Analysis in India using Deep Learning

Traffic congestion is a common affair in the big cities and towns. This issue is the outcome of the rapid increase in the population and increasing number of vehicles, so predicting the level of traffic congestion will be beneficial for every individual. However, interpretation and implementation of traffic state can be exceptionally tough. With this pace of increasing vehicles, existing algorithms may come up with some limitations due to various aspects of features which we cannot process. In this paper, we introduce a Hybrid Deep Neural Network (HDNN) for forecasting the traffic conditions on roads with the images using Convolutional Neural Network (CNN) and predicting road accident statistics of a particular area on a specific time. This model will exploit the development of algorithms in machine learning and majorly grasping over the Deep learning algorithm CNN. Experimental results show superior results of traffic conditions prediction and road accidentsanalysis, HDNN outshine the standard benchmark for the level of traffic congestion.
Total-Ionizing-Dose Effects on InGaAs FinFETs With Modified Gate-stack

We evaluate the total-ionizing-dose (TID) responses of InGaAs nMOS fin field-effect transistors (FinFETs) with a modified gate-stack irradiated with 10-keV X-rays under different gate biases. This modified InGaAs nMOS FinFET process shows decreased subthreshold leakage current and increased hysteresis in as-processed devices, and reduced hole trapping in irradiated devices, than first-generation development-stage devices. The reduction in subthreshold leakage current is attributed to changing the buffer layer from GaAs to In<sub>0.3</sub>Ga<sub>0.7</sub>As, thereby enhancing the material quality. Both the increased hysteresis in as-processed devices and reduced hole trapping in irradiated devices are attributed primarily to thinning the Al<sub>2</sub>O<sub>3</sub> layer that separates the HfO<sub>2</sub> from the InGaAs layers. This facilitates charge exchange with defects at the HfO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> interface and reduces the percentage of radiation-induced holes that are generated in Al<sub>2</sub>O<sub>3</sub> and trapped in HfO<sub>2</sub>. The removal of a tungsten layer above the TiN gate reduces the interface dose enhancement.
Predicting Forward & Backward Facial Depth Maps From a Single RGB Image For Mobile 3d AR Application

Cheap and fast 3D asset creation to enable AR/VR applications is a fast growing domain. This paper addresses a significant problem of reconstructing complete 3D information of a face in near real-time speed on a mobile phone. We propose a novel deep learning based solution to predict robust depth maps of a face, one forward facing and the other backward facing, from a single image from the wild. A critical contribution is that the proposed network is capable of learning the depths of the occluded part of the face too. This is achieved by training a fully convolutional neural network to learn the dual (forward and backward) depth maps, with a common encoder and two separate decoders. The 300W-LP, a cloud point dataset, is used to compute the required dual depth maps from the training data. The code and results will be made available at project page.
A Broiler Stress Detection System Based on Audio Signal Processing

The paper presents a system for stress detection in broiler chickens using audio data. It should be part of the precision livestock farming system whose goal is to maximize profit and product quality without harming animal wellbeing. The system is consisted of 4 classifiers adapted for 4 age groups of chickens (one for each week). These classifiers are based on support vector machines and as input features they use the features for voice quality evaluation and speech emotion recognition. Accuracy on the 50ms frame level of these classifiers varies from 63 to 83 %, depending on age group.
A Petri Net Neural Network Robust Control for New Paste Backfill Process Model

In mining industries, the backfill becomes more and more important due to the environment protection concern. But most backfill investigations focus on the underground model and backfill materials. In this research, the paste backfill process based on the process control is investigated and a new paste backfill process model is proposed according to the Torricelli’s law and Bernoulli principle. In order to deal with the unknown nonlinear function of the proposed backfill model, a Petri net(PN) structure is presented and a new Petri net neural network(PNNN) is introduced to approximate the unknown nonlinear function. Then, a robust controller is designed for the backfill process with PNNN and the closed loop stability is guaranteed by Lyapunov function candidate. The effectiveness of the proposed model with new PNNN and the robust controller are verified by simulation results.