Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving High Mobility and Excellent Stability in Amorphous In–Ga–Zn–Sn–O Thin-Film Transistors

This article reports the fabrication of high-performance amorphous indium gallium zinc tin oxide (<inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>-IGZTO) thin-film transistors (TFTs) with superior bias stability. For comparison, amorphous indium gallium zinc oxide (<inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>-IGZO) TFTs were also investigated to clarify the origin of the superior performance of IGZTO TFTs. It was found that the simultaneous heavy loading of In and Sn into the IGZTO system facilitated an effective mass densification, leading to a reduction in tail states and deep states. The fabricated <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>-IGZTO TFTs exhibited a high electron mobility (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\mu }_{\text {FE}}$ </tex-math></inline-formula>) of 46.7 cm<sup>2</sup>/Vs, a subthreshold swing (SS) gate of 0.15 V/decade, and an <inline-formula> <tex-math notation="LaTeX">${I}_{\text {ON/OFF}}$ </tex-math></inline-formula> ratio <inline-formula> <tex-math notation="LaTeX">$> {1} \times {10}^{{8}}$ </tex-math></inline-formula>. Furthermore, greater gate-bias stress stability was observed for the IGZTO TFTs compared with the IGZO TFTs.
Study of OAM mode purity by changing the orientation of antenna element

Orbital angular momentum concept has discussed with the help of mathematical equations. An investigation of 8 elements uniform circular array (UCA) for different Orbital Angular Momentum (OAM) modes is carried out for three designs. The effect of array radius on OAM purity and the radial outward design to reduce phase complexity is discussed. Uniform circular antenna array for all possible combination has been simulated and its results are demonstrated for different physical and electrical characteristic.
Giant cell tumors of the child’s fibula: a case study

We report an exceptional case of Giant Cell Tumor (GCT) in child. A twelve –year-old girl which presented a tumefaction at the lower extremity of her right leg. The clinical examination revealed a good general state of health, a non inflammatory tumor at the latero-external side of his right leg. A standard X-ray photography showed a bee nest like osteolysis lesion at the lower fibula metaphyse. A biopsy was performed and confirmed the diagnosis of a GCT. The treatment consisted in a large ablation of the tumor including part of the healthy zone, followed by an autologous iliac bone graft, stabilized with a Metaizeau pin and a cruropedal plaster. The evolution was favourable after a period of two years. The case we are presenting is fairly exceptional because of its location and the age of the patient.
Double E-Shaped Wideband Microstrip Patch Antenna for Wireless Applications

A wideband double E-shaped antenna with co-axial fed resonating at 5.21 GHz, 5.5 GHz, 5.85 GHz and 6.3 GHz is presented. The proposed configuration is intended to cater a wide range of bandwidth nominally upto 1.4 GHz which is the demanding performance parameter for wireless communication.
Gate Detection for Micro Aerial Vehicles using a Single Shot Detector

Object detection has become an essential tool in aerial robotics thanks to the use of onboard cameras in drones that enables find objects using techniques of vision. However, vision algorithms may become unreliable presenting drawback by the illumination changes. Deep learning has been used to solve tasks of classification, segmentation and detection using traditional Convolutional Neural Network (CNN) like VGG16, YOLO and AlexNet. This paper presents a gates detector system in a real-time using CNN based on a Single Shot Detector Network (SSD) for drone racing circuits. For the latter, we have adopted the SSD7 architecture to modified and present an implementation with five layers, reducing the prediction time and improve detection velocity in comparison with other architectures. For evaluation purpose, we selected three environments: simulation, indoors and outdoors to compare the prediction time, average fps and the confidence obtained in the detections of the gates.
Study on Accumulation Effect of Multiple Lightning Impulse on Vegetable Oil-Paper Insulation

Vegetable insulation oil has been widely used as a good liquid dielectric for oil-paper insulation power equipment. During the operational life, the oil-paper insulation power equipment can suffer from hundreds of lightning impulse, which cause insulation damage. Thus, study on the accumulation effect of multiple lightning impulse on vegetable oil-paper insulation is significance. However, few literature studies on the impact of lightning impulse accumulation on vegetable oil-paper insulation. In this paper, the accumulation effects of multiple lightning impulse on vegetable oil-paper insulation are studied. The Voltage-Number (V-N) characteristics and two-parameter Weibull distribution are used to evaluate the insulation performance of rapeseed oil-paper insulation and calculate the cumulative failure probability. The dielectric properties of the oil-impregnated insulating paper were measured to analyze the insulation damage and deterioration. Results shows that dielectric constant, dielectric loss factor and volume resistivity increases with increasing cumulative number of lightning impulses.
A Novel Grouping Mechanism for Performance Enhancement of Sub-1 GHz Wireless Networks

IEEE 802.11ah is a new wireless LAN technology proposed mainly for IoT (Internet of Things). Operating at frequency band under 1 GHz, 802.11ah WLAN can provide much extended coverage range and support around 8,000 nodes to be associated with a single AP. Such a dense environment may cause hidden terminal problems which can severely degrade the performance of the network. Thus, the 802.11ah standard adopts a group based MAC protocol such that the nodes are divided randomly into several groups and members of a group contend to access channel within their allocated RAWs (Restricted Access Windows). However, the random grouping method cannot solve the hidden terminal problem sufficiently. In this paper, we propose a novel grouping mechanism that groups the nodes by their hidden-terminal relationship: i.e., any two nodes hidden to each other are assigned to different groups, and thus access the channel using different RAWs. Mathematic analysis and simulations show that our proposed scheme remarkably improve the performance of Sub-1 GHz WLAN.
Effect of Surfactant on Dielectric and Thermal Properties of Silicone Nanocomposites

Experimental studies have been carried out on silicone nanocomposites to understand the effect of mixing method and filler treatment specifically, the use of surfactant on the performance of synthesized composites. Silicone rubber, RTV 615 is mixed with treated 10 wt% nano fumed silica to form silicone nanocomposites. Two types of filler treatment, namely calcination and adding surfactant, and two types of processing methods, electrostatic disperser (ED) and high shear (HS) mixing, are used to disperse the fillers. Structural, thermal and dielectric analyses of the nanocomposites are conducted on a total of five compositions, consisting of the same resin, filler, and filler weight, using the above-mentioned filler dispersion techniques. Calcination of fillers and the use of an electrostatic disperser for mixing have no adverse effects on the performance of composites, compared to use of surfactant. The results show that using surfactant impacts both the dielectric and thermal properties of nanocomposites due to a large number of weak interfaces.
Design and Analysis of Neural Networks Based on Linearly Translated Features

In this paper, neural networks based on linearly translated features (LTFs) are presented. LTFs including uniform, non-uniform, and multiple translation vectors are embedded into feedforward neural networks. Learning algorithms are presented for the neural networks. Learning capabilities of the neural networks are analyzed. Experimental results on approximation’ identification, and evaluation problems are reported to substantiate the efficacy of the neural networks and learning algorithms.
NFV and SDN-Based Differentiated Traffic Treatment for Residential Networks

Residential networks play a critical role in assuring that services or applications such as tele-work, tele-education, medical care, entertainment, home automation, among others, have the required resources to obtain an optimal performance. Although current residential gateways try to meet the Quality of Service (QoS) demands, the traditional networking paradigm does not have the appropriate mechanisms to address the heterogeneous and dynamic nature of the services running at home. In this context, a feasible solution consists of leveraging the flexibility and adaptability of the Software Defined Networking (SDN) and Network Functions Virtualization (NFV) paradigms to provide a differentiated traffic treatment intended to improve the QoS support of residential networks. The proposal takes advantage of the Service Function Chaining (SFC) concept intrinsic to NFV as well as the capacity of an SDN-based residential gateway to differentiate the traffic of a certain application. Thus, an association between an SFC and the differentiated traffic is stablished to apply a specific treatment. Besides, a comprehensive architecture composed of the software defined residential network (SDRN), the software defined access network (SDOAN) and the NFV-compliant ISP’s edge cloud infrastructure is envisioned. This architecture would allow dramatically improving the life cycle management of the residential network from a centralized point which follows a user-centric approach.
Cascaded Bearing Only SLAM with Uniform Semi-Global Asymptotic Stability

Bearing only simultaneous localization and mapping (SLAM) is an extensively researched topic, where the nonlinearity of the problem leads to consistency, bias and stability challenges for the filter. In this article we combine two newly developed filters for bearing-only SLAM, with global stability results in a cascade structure; inspired by the newly presented eXogenous Kalman filter (XKF). We prove that this cascade structure has uniformly semi-globally asymptotic stability (USGAS), and through simulations, we show how this setup can increase the robustness of the filtering against nonlinear measurement noise. In addition, it is shown how the nonlinear observer smooths the bearing measurements.
Power Loss in SMES Coil as Influenced by its Material

Superconducting magnetic energy storage (SMES) system is one of the commonly used technique by the end users to mitigate the voltage sag at their premises from the distribution system. The SMES is a superconductor coil wound on a nonmagnetic core. Contribution of refrigeration and AC loss in total SMES coil loss at different output power rating is investigated. Different Low Temperature Superconductivity (LTS) coil materials at operating temperature of $4^\circ\mathrm {K}$ are considered to select the best material with minimum coil loss. The results concluded that Nb-Ti material achieved minimum coil loss when compared with $\mathrm {M}\mathrm {g}\mathrm {B}_{2}$ and YBCO.
Maximum Likelihood Detection in Focal Plane Arrays with Generic Point Spread Function (Poster)

In this paper the problem of target detection on images and focal plane arrays (FPA) is considered. The proposed approach has applications in biomedical systems, autonomous surveillance systems, target tracking systems and robotics. In a previous paper at the Fusion conference the problem of single, point target detection on FPA was solved under the assumption that the point spread function of the target is strictly circular. The proposed approach in this paper extends the previous result for a generic point spread function that is more applicable to solve practical problems. In this paper, we derive the maximum likelihood (ML) target detector for image observations; the proposed ML detector is optimal under the generic assumption that the FPA contains a single target that is in the form of a Gaussian signal intensity with known covariance Matrix. Further, we derive the Cramer-Rao lower bound (CRLB) of the estimation and then present the hypothesis test to find a threshold for target acceptance. Finally, we theoretically derive the receiver operating characteristic (ROC) curve of the detector. Simulation results show that the ML estimator is efficient and that the theoretically derived ROC is a close approximation to the realistic one at very low signal to noise ratio values.
Towards the Optimal Solution for the Routing Problem in Vehicular Delay Tolerant Networks: A Deep Learning Approach

Vehicular networks aim to provide a communication framework for moving vehicles, road infrastructure and pedestrians. Such kind of networks is the entrance to a new era of services that will make areas such as security and safety, information, transactions, entertainment and sustainability (green transportation) more efficient than they are today, especially in the upcoming era of autonomous vehicles and self-driving cars. However, the severe nature of vehicular environments makes efficient inter-vehicular communication very difficult to achieve. Vehicular Delay-Tolerant Networks (VDTN), as these networks are called, have very sparse, intermittent connections, and the lack of a fixed topology gives rise to one of the main challenges that they face: packet routing. A range of routing algorithms has been proposed in recent years to optimize communication in vehicular networks, and significant progress has been made in the matter but the quest for the optimal performance is still ongoing. In this paper, we explore a deep learning approach to the routing problem in VDTN, proposing a routing architecture and algorithm based on Deep Neural Networks that helps routers make packet forwarding decisions based on the current conditions of its surroundings. In order to assess the performance of the proposed architecture, simulations were run showing important gains in terms of network overhead and hop count with respect to popular routers, while maintaining acceptable packet delivery rates and average delivery delays.
Micro Grid: An Energy Efficient Approach for Modern Power System

Renewable Energy Generation is a fast growing field of study under Power System Engineering. Renewable Energy has gained much popularity due to the fact that it can be easily setup near load centres and have very low running cost. Renewable Energy Generation also has the added advantage that it is environment friendly and will help in sustained development. This paper intends to study the interaction of multiple renewable sources when it is operated together to supply AC as well as DC Loads. This paper studies the behaviour of micro-grid in grid connected mode of operation. Bi-Directional operation of converter is also studies using decoupled vector control. Centralized Micro-Grid Controller is used for the Energy Management of the test system. Solar and Wind are the two renewable energy sources selected for study in this paper. Battery is also used in this system for energy storage. AC as well as DC loads are connected to the system to study the flow of energy.
Cognitive Radar Tracking Performance Enhancement via Waveform Optimization

This paper addresses target tracking using cognitive radars and discusses the effect of radar waveform parameters optimization on the tracking performance. Flying targets are not typically governed by one uniform motion model but rather can go through a diversity of arbitrary manoeuvres. Facing the change in the target dynamics, it is necessary to design the radar parameters adaptively to guarantee a better performance. To counterbalance the target motion model uncertainty, we are proposing an IMM-based algorithm for target tracking and waveform optimization to allow the cognitive radar to adapt to the continuous changes of the target dynamics. In this approach the target state vector estimate resulting from the IMM algorithm will be taken to adaptively change the radar parameters such as the radar pulse repetition frequency (PRF), the number of integrated pulses N, the radar scan schedule and the detection threshold. Simulation results will show that optimizing the radar waveform parameters enhances the radar tracking performance.
Hybrid POS Classification Using Long Short Term Memory

A Part of Speech classifier is an important tool that is used to develop many NLP tasks. In this paper we described Deep Neural Network based architecture, that given a sentence, outputs a part-of-speech tag sequence. For Part of Speech classification, we trained a LSTM network by semi-supervised learning. We further applied a probabilistic approach to improve Part of Speech prediction of the LSTM network. Word2vec deep learning tool is used for learning high-quality distributed vector representations of words that capture a large number of precise word relationships. By using a small amount of labeled dataset of words and applying cosine similarity we assign Part of Speech tags to words. If some words don't show nearness to any word in labeled dataset, then we apply probabilistic approach for classification of unclassified words of input sentence. This hybrid neural Part of Speech classifier is evaluated over a corpus of 1000 sentences having a total of 7,400 words; part of speech classification system achieved the accuracy of 94.71 %.
Optimal Power Allocation for Secure Transmission with Both Internal and External Eavesdroppers

A cooperative jamming and beamforming scheme is proposed for secrecy transmission. Different from the existing works, an internal eavesdropper (Eve) and multiple external Eves coexist in the system, where a multi-antenna jammer is deployed to confuse Eves by using artificial noise. Specifically, the jammer can obtain the instantaneous channel state information (CSI) of the internal Eve, but only has the partial CSI of the external Eves. Upon setting up, the transmission outage requirement at Bob and secrecy outage constraint at the Eves, the transmit power is optimized to derive the maximum secrecy rate for the transmission. Moreover, we investigate the optimal power allocation scheme by characterizing the beamforming vectors under considering both perfect and imperfect CSI cases for the internal Eve. Numerical results show that the proposed scheme can improve the secrecy rate significantly by making full use of instantaneous CSI between the jammer and the internal Eve.
Stochastic Optimization for Pricing-Aware Multimedia Services in 5G Vehicular Networks

The many fold capacity magnification promised by 5G vehicular networks will likely provide massive multimedia services, including infotainment, augmented reality, location services, etc. However, the large-scale and stochastic characteristic of these burgeoning multimedia applications will lead to an exponential increase of traffic in vehicular networks. Meanwhile, the diversified requirements introduced by the coexistence with traditional services will also bring new challenges to the efficient usage of resources. To cope with the above challenges, we propose a novel Stochastic Optimization framework for Pricing-aware Multimedia Services (SOPMS) in this paper, which targets the maximization of utility with the constraints of system stability and traffic pricing policy. Specifically, we leverage the Lyapunov function to address this optimization objective, which is decomposed into three tractable subproblems. For each problem, a distinct algorithm is conceived, i.e. Quality of Experience (QoE) based utility maximization, cooperative resource allocation and pricing-based transmission control. Finally, validated by the simulations, our proposed SOPMS preserves the optimality and significantly improves the queue stability and service utility, in comparison with other state-of-the-art solutions.
Human Drowsiness Detection In Real Time, Using Computer Vision

This paper presents a human drowsiness detection algorithm in real time using computer vision. Drowsiness is a state whose consequences can be very dangerous for vehicle drivers, air traffic controllers, nuclear plant controllers, etc. In 2018 in Ecuador, 353 traffic accidents were reported for driving while drowsy. The algorithm that we present obtains frontal images of the driver using an infrared camera, then performs automatic face detection using the Viola-Jones algorithm. After this, the eye portion is extracted and the classification between open and closed eye is done with two methods: a) method based on the extraction of maximums and minimums of horizontal and vertical edges of the eye and b) using a multilayer perceptron (MLP) neural network. Finally, it makes the detection of drowsiness during the time the eyes were closed within a time interval. For the open and close eye classification using the first method we obtain 84% of accuracy and for the second method using the MLP we obtain 97% of accuracy, including test images under dark conditions.
Research of Authentication Problems on Reliable Multi-Hop Broadcast Protocol for Vehicular Networking

In the vehicular networking, the urgent alarm message warnings can be widely distributed by designing reliable multi-hop broadcast protocol. The warnings are important for improving the road traffic safety. The key to realize the reliable multi-hop broadcast is to dynamically select the optimal relay vehicles. In order to distribute warnings fast and reliably, the transmission reliability and real-time performance impacted by the transmit power control technique of the moving vehicle are studied in this paper. Also, a reliable broadcast protocol RMBP based on the delay perception is designed.
Ultra low voltage, low power active-RC filter in 90 nm CMOS technology

In this work, an ultra low voltage, second order low pass active-RC filter is designed in 90 nm CMOS process. A bulk-driven transconductor and a feed-forward compensated operational transconductance amplifier (OTA) are used as building blocks of the filter. Operating with a supply voltage of 0.5 V, filter exhibits a low pass Butterworth response with a cutoff frequency of 1 MHz. A mathematical model of the filter is developed using descriptor state-space equations for optimizing the filter response. The designed filter consumes a power as less as 91.99 μW with a figure of merit (FoM) of 3.39 fJ/pole.
Pontus: A Linguistics-Based DGA Detection System

Many botmasters use domain generation algorithms (DGA) to generate a host of malicious algorithmically- generated domains (mAGDs) and then choose several mAGDs for actual command and control (C2) communication. The botmasters use different seeds (e.g., timestamp) to generate different mAGDs, which makes the communication mechanism resilient to blacklisting. Thus the botmasters can hide C2 channels very well. If we can quickly detect these mAGDs from DNS traffic, we will effectively block the communication. In this paper, we propose a novel system, called Pontus, to detect mAGDs from DNS traffic. Pontus extract features exclusively from the individual domain names. We compare Pontus with the state-of-the-art system and find that Pontus improves the precision by at least 4.7%.